descriptive research topics in medical technology

399+ Amazing Medtech Research Topics

Get ready to see the world of medical technology with our collection of 399+ Amazing Medtech Research Topics. We’ve got the knowledge on cutting-edge subjects that impact healthcare, from artificial intelligence in medicine to innovative drug delivery systems.

No complicated texts, just straightforward insights into the future of medical technology. Whether you’re curious about advancements in imaging, wearable health tech, or the potential of 3D printing in medicine, this list has it all.

It’s like having a roadmap to the latest trends and breakthroughs in the medical field. So, if you’re keen on staying in the know about what’s shaping the future of healthcare, you’re in the right place. Let’s go on this journey and analyze these medtech research topics.

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What Is Medtech?

Table of Contents

Medtech, used for medical technology, refers to the use of technology, devices, and innovations in healthcare to improve diagnosis, treatment, and overall patient care.

It includes numerous tools and equipment, from medical imaging devices to wearable health gadgets and advanced surgical instruments. Medtech aims to enhance the effectiveness of healthcare practices, provide more accurate diagnostics, and contribute to better patient outcomes.

In simple terms, medtech combines technology with medical expertise to create solutions that benefit both healthcare professionals and patients.

Importance Of Medtech In Current Scenario

Medtech plays an important role in the current healthcare landscape, offering several key advantages:

Enhanced Diagnostics

Medtech advancements provide more accurate and swift diagnostic tools, aiding healthcare professionals in identifying illnesses at earlier stages for timely intervention.

Remote Monitoring

Medtech enables remote patient monitoring, allowing healthcare providers to track patients’ health in real-time and intervene promptly, especially beneficial in managing chronic conditions.

Efficiency and Precision in Surgery

Surgical procedures benefit from precision instruments and robotics, leading to minimally invasive surgeries, quicker recovery times, and reduced risks.

Access to Healthcare

Medtech facilitates telemedicine and telehealth solutions, making healthcare services more accessible to remote or underserved populations.

Data Management and Analysis

Digital health technologies allows data management, fostering efficient analysis for research, treatment optimization, and public health planning.

Preventive Healthcare

Wearable devices and health apps allows individuals to monitor their health, promoting preventive measures and healthier lifestyles.

Drug Delivery Systems

Medtech innovations contribute to more efficient and targeted drug delivery, improving the effectiveness of medications while minimizing side effects.

Cost-Effective Solutions

In the long run, medtech investments can make it possible to save costs by decreasing hospital stays, preventing complications, and optimizing resource utilization.

In conclusion, the importance of medtech in the current scenario lies in its ability to revolutionize healthcare by making it more accurate, accessible, and patient-centric. These technologies contribute significantly to improving both the quality and efficiency of healthcare services worldwide.

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77 interesting medical research topics for 2024

Last updated

25 November 2023

Reviewed by

Brittany Ferri, PhD, OTR/L

Short on time? Get an AI generated summary of this article instead

Medical research is the gateway to improved patient care and expanding our available treatment options. However, finding a relevant and compelling research topic can be challenging.

Use this article as a jumping-off point to select an interesting medical research topic for your next paper or clinical study.

How to choose a medical research topic

When choosing a research topic , it’s essential to consider a couple of things. What topics interest you? What unanswered questions do you want to address?

During the decision-making and brainstorming process, here are a few helpful tips to help you pick the right medical research topic:

Focus on a particular field of study

The best medical research is specific to a particular area. Generalized studies are often too broad to produce meaningful results, so we advise picking a specific niche early in the process.

Maybe a certain topic interests you, or your industry knowledge reveals areas of need.

Look into commonly researched topics

Once you’ve chosen your research field, do some preliminary research. What have other academics done in their papers and projects?

From this list, you can focus on specific topics that interest you without accidentally creating a copycat project. This groundwork will also help you uncover any literature gaps—those may be beneficial areas for research.

Get curious and ask questions

Now you can get curious. Ask questions that start with why, how, or what. These questions are the starting point of your project design and will act as your guiding light throughout the process.

For example:

What impact does pollution have on children’s lung function in inner-city neighborhoods?

Why is pollution-based asthma on the rise?

How can we address pollution-induced asthma in young children?

77 medical research topics worth exploring in 2023

Need some research inspiration for your upcoming paper or clinical study? We’ve compiled a list of 77 topical and in-demand medical research ideas. Let’s take a look.

Exciting new medical research topics

If you want to study cutting-edge topics, here are some exciting options:

COVID-19 and long COVID symptoms

Since 2020, COVID-19 has been a hot-button topic in medicine, along with the long-term symptoms in those with a history of COVID-19.

Examples of COVID-19-related research topics worth exploring include:

The long-term impact of COVID-19 on cardiac and respiratory health

COVID-19 vaccination rates

The evolution of COVID-19 symptoms over time

New variants and strains of the COVID-19 virus

Changes in social behavior and public health regulations amid COVID-19

Vaccinations

Finding ways to cure or reduce the disease burden of chronic infectious diseases is a crucial research area. Vaccination is a powerful option and a great topic to research.

Examples of vaccination-related research topics include:

mRNA vaccines for viral infections

Biomaterial vaccination capabilities

Vaccination rates based on location, ethnicity, or age

Public opinion about vaccination safety

Artificial tissues fabrication

With the need for donor organs increasing, finding ways to fabricate artificial bioactive tissues (and possibly organs) is a popular research area.

Examples of artificial tissue-related research topics you can study include:

The viability of artificially printed tissues

Tissue substrate and building block material studies

The ethics and efficacy of artificial tissue creation

Medical research topics for medical students

For many medical students, research is a big driver for entering healthcare. If you’re a medical student looking for a research topic, here are some great ideas to work from:

Sleep disorders

Poor sleep quality is a growing problem, and it can significantly impact a person’s overall health.

Examples of sleep disorder-related research topics include:

How stress affects sleep quality

The prevalence and impact of insomnia on patients with mental health conditions

Possible triggers for sleep disorder development

The impact of poor sleep quality on psychological and physical health

How melatonin supplements impact sleep quality

Alzheimer’s and dementia

Cognitive conditions like dementia and Alzheimer’s disease are on the rise worldwide. They currently have no cure. As a result, research about these topics is in high demand.

Examples of dementia-related research topics you could explore include:

The prevalence of Alzheimer’s disease in a chosen population

Early onset symptoms of dementia

Possible triggers or causes of cognitive decline with age

Treatment options for dementia-like conditions

The mental and physical burden of caregiving for patients with dementia

Lifestyle habits and public health

Modern lifestyles have profoundly impacted the average person’s daily habits, and plenty of interesting topics explore its effects.

Examples of lifestyle and public health-related research topics include:

The nutritional intake of college students

The impact of chronic work stress on overall health

The rise of upper back and neck pain from laptop use

Prevalence and cause of repetitive strain injuries (RSI)

Controversial medical research paper topics

Medical research is a hotbed of controversial topics, content, and areas of study.

If you want to explore a more niche (and attention-grabbing) concept, here are some controversial medical research topics worth looking into:

The benefits and risks of medical cannabis

Depending on where you live, the legalization and use of cannabis for medical conditions is controversial for the general public and healthcare providers.

Examples of medical cannabis-related research topics that might grab your attention include:

The legalization process of medical cannabis

The impact of cannabis use on developmental milestones in youth users

Cannabis and mental health diagnoses

CBD’s impact on chronic pain

Prevalence of cannabis use in young people

The impact of maternal cannabis use on fetal development

Understanding how THC impacts cognitive function

Human genetics

The Human Genome Project identified, mapped, and sequenced all human DNA genes. Its completion in 2003 opened up a world of exciting and controversial studies in human genetics.

Examples of human genetics-related research topics worth delving into include:

Medical genetics and the incidence of genetic-based health disorders

Behavioral genetics differences between identical twins

Genetic risk factors for neurodegenerative disorders

Machine learning technologies for genetic research

Sexual health studies

Human sexuality and sexual health are important (yet often stigmatized) medical topics that need new research and analysis.

As a diverse field ranging from sexual orientation studies to sexual pathophysiology, examples of sexual health-related research topics include:

The incidence of sexually transmitted infections within a chosen population

Mental health conditions within the LGBTQIA+ community

The impact of untreated sexually transmitted infections

Access to safe sex resources (condoms, dental dams, etc.) in rural areas

Health and wellness research topics

Human wellness and health are trendy topics in modern medicine as more people are interested in finding natural ways to live healthier lifestyles.

If this field of study interests you, here are some big topics in the wellness space:

Gluten sensitivity

Gluten allergies and intolerances have risen over the past few decades. If you’re interested in exploring this topic, your options range in severity from mild gastrointestinal symptoms to full-blown anaphylaxis.

Some examples of gluten sensitivity-related research topics include:

The pathophysiology and incidence of Celiac disease

Early onset symptoms of gluten intolerance

The prevalence of gluten allergies within a set population

Gluten allergies and the incidence of other gastrointestinal health conditions

Pollution and lung health

Living in large urban cities means regular exposure to high levels of pollutants.

As more people become interested in protecting their lung health, examples of impactful lung health and pollution-related research topics include:

The extent of pollution in densely packed urban areas

The prevalence of pollution-based asthma in a set population

Lung capacity and function in young people

The benefits and risks of steroid therapy for asthma

Pollution risks based on geographical location

Plant-based diets

Plant-based diets like vegan and paleo diets are emerging trends in healthcare due to their limited supporting research.

If you’re interested in learning more about the potential benefits or risks of holistic, diet-based medicine, examples of plant-based diet research topics to explore include:

Vegan and plant-based diets as part of disease management

Potential risks and benefits of specific plant-based diets

Plant-based diets and their impact on body mass index

The effect of diet and lifestyle on chronic disease management

Health supplements

Supplements are a multi-billion dollar industry. Many health-conscious people take supplements, including vitamins, minerals, herbal medicine, and more.

Examples of health supplement-related research topics worth investigating include:

Omega-3 fish oil safety and efficacy for cardiac patients

The benefits and risks of regular vitamin D supplementation

Health supplementation regulation and product quality

The impact of social influencer marketing on consumer supplement practices

Analyzing added ingredients in protein powders

Healthcare research topics

Working within the healthcare industry means you have insider knowledge and opportunity. Maybe you’d like to research the overall system, administration, and inherent biases that disrupt access to quality care.

While these topics are essential to explore, it is important to note that these studies usually require approval and oversight from an Institutional Review Board (IRB). This ensures the study is ethical and does not harm any subjects.

For this reason, the IRB sets protocols that require additional planning, so consider this when mapping out your study’s timeline.

Here are some examples of trending healthcare research areas worth pursuing:

The pros and cons of electronic health records

The rise of electronic healthcare charting and records has forever changed how medical professionals and patients interact with their health data.

Examples of electronic health record-related research topics include:

The number of medication errors reported during a software switch

Nurse sentiment analysis of electronic charting practices

Ethical and legal studies into encrypting and storing personal health data

Inequities within healthcare access

Many barriers inhibit people from accessing the quality medical care they need. These issues result in health disparities and injustices.

Examples of research topics about health inequities include:

The impact of social determinants of health in a set population

Early and late-stage cancer stage diagnosis in urban vs. rural populations

Affordability of life-saving medications

Health insurance limitations and their impact on overall health

Diagnostic and treatment rates across ethnicities

People who belong to an ethnic minority are more likely to experience barriers and restrictions when trying to receive quality medical care. This is due to systemic healthcare racism and bias.

As a result, diagnostic and treatment rates in minority populations are a hot-button field of research. Examples of ethnicity-based research topics include:

Cancer biopsy rates in BIPOC women

The prevalence of diabetes in Indigenous communities

Access inequalities in women’s health preventative screenings

The prevalence of undiagnosed hypertension in Black populations

Pharmaceutical research topics

Large pharmaceutical companies are incredibly interested in investing in research to learn more about potential cures and treatments for diseases.

If you’re interested in building a career in pharmaceutical research, here are a few examples of in-demand research topics:

Cancer treatment options

Clinical research is in high demand as pharmaceutical companies explore novel cancer treatment options outside of chemotherapy and radiation.

Examples of cancer treatment-related research topics include:

Stem cell therapy for cancer

Oncogenic gene dysregulation and its impact on disease

Cancer-causing viral agents and their risks

Treatment efficacy based on early vs. late-stage cancer diagnosis

Cancer vaccines and targeted therapies

Immunotherapy for cancer

Pain medication alternatives

Historically, opioid medications were the primary treatment for short- and long-term pain. But, with the opioid epidemic getting worse, the need for alternative pain medications has never been more urgent.

Examples of pain medication-related research topics include:

Opioid withdrawal symptoms and risks

Early signs of pain medication misuse

Anti-inflammatory medications for pain control

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Explore the Best Medical and Health Research Topics Ideas

Table of contents

1 How to Choose Medical Research Paper Topics
2 New Medical Research Paper Topics
3 Medical Research Topics for College Students
4 Controversial Medical Topics for Research Paper
5 Health Research Topics
6 Medicine Research Topics
7 Healthcare Research Topics
8 Public Health Research Topics
9 Mental Health Research Paper Topics
10 Anatomy Research Topics
11 Biomedical Research Topics
12 Bioethics Research Topics
13 Cancer Research Topics
14 Clinical Research Topics
15 Critical Care Research Topics
16 Pediatric Research Topics
17 Dental Research Topics Ideas
18 Dermatology Research Topics
19 Primary Care Research Topics
20 Pharmaceutical Research Topics
21 Medical Anthropology Research Topics
22 Paramedic Research Paper Topics
23 Surgery Research Topics
24 Radiology Research Paper Topics
25 Anatomy and Physiology Research Paper Topics
26 Healthcare Management Research Paper Topics
27 Medical Ethics Research Paper Topics
28 Environmental Health and Pollution Research Paper Topics
29 Conclusion

Writing an original and compelling research paper is a daunting task in such a complex and broad field as medicine. Each student decides where his interests lie, from investigating public care concerns to cancer treatment studies. We aim to help students find new angles to study and focus on relevant topics. With our resources, you can write an engaging and rigorous paper.

How to Choose Medical Research Paper Topics

Choosing good research paper topics is often more challenging than the writing process itself. You need to select a captivating subject matter that will grab the reader’s attention, showcase your knowledge of a specific field, help you progress in your studies, and perhaps even inspire future research.

To accomplish that, you need to start with brainstorming, followed by thorough research. Here are some great tips to follow:

Pick an interesting topic – The key is to pick something that you find interesting, and yet make sure it’s not too general or too narrow. It should allow you to delve deep into the subject matter and show that you’re a professional who is ready to take on a challenge when it comes to your chosen field of medicine.
Narrow down your focus – Once you have a list of potential topics, sift through recent medical research papers to get up-to-date with the latest trends, developments, and issues in medicine and healthcare. Check out textbooks, news articles, and other relevant sources for more information related to your potential topics. If a particular condition or disease interests you (perhaps something that drew you to a career in medicine), there’s your cue for narrowing down your topic.
Pinpoint the “why,” “how,” and “what” – Whether you are looking into nutrition research paper topics , controversial medical topics, nursing research topics, or anything in-between, ask yourself why each of them is important. How could they contribute to the available medical studies, if any? What new information could they bring to improve the future of medicine? Asking these questions will help you pick the right medical research paper topic that suits you and helps you move forward and reach your aspirations.

To help you on that quest, we’ve compiled a list of topics that you could use or that might inspire you to come up with something unique. Let’s dive in.

New Medical Research Paper Topics

Are you interested in the newest and most interesting developments in medicine? We put hours of effort into identifying the current trends in health research so we could provide you with these examples of topics. Whether you hire a research paper writing service for students or write a paper by yourself, you need an appealing topic to focus on.

Epidemics versus pandemics
Child health care
Medical humanitarian missions in the developing world
Effectiveness of mobile health clinics in rural Africa
Homeopathic medicines – the placebo effect
Comparative study of the efficacy of homeopathic treatments and conventional medicine in managing chronic pain
Virus infections – causes and treatment
Trends in COVID-19 vaccine uptake
Advancements in the treatment of influenza
Is medical research on animals ethical
Vaccination – dangers versus benefits
Artificial tissues and organs
Rare genetic diseases
Brain injuries
Long-Term Effects of COVID-19
Social behavior shifts due to COVID-19

Medical Research Topics for College Students

You don’t know where to start with your medical research paper? There are so many things you could write about that the greatest challenge is to narrow them down. This is why we decided to help.

Antibiotics treatments
Efficacy of mRNA vaccines against viral diseases
Viability and function of 3D printed tissues
Chronic diseases
Palliative treatment
Battling Alzheimer’s disease
How modern lifestyle affects public health
Professional diseases
Sleep disorders
Changes in physical and mental health due to aging
Eating disorders
Terminal diseases

Controversial Medical Topics for Research Paper

In healthcare, new discoveries can change people’s lives in the blink of an eye. This is also the reason why there are so many controversial topics in medicine, which involve anything from religion to ethics or social responsibility. Read on to discover our top controversial research topics.

Ethical debates on artificial tissue engineering
Public opinions on vaccination safety
Implementing food standards
Telehealth’s Role in Chronic Illness Management
Gluten allergy
Assisted suicide for terminal patients
Testing vaccines on animals – ethical concerns
Moral responsibilities regarding cloning
Marijuana legalization for medical purposes
Abortion – medical approaches
Vegan diets – benefits and dangers
Increased life expectancy: a burden on the healthcare system?
Circumcision effects

Health Research Topics

Students conducting health research struggle with finding good ideas related to their medical interests. If you want to write interesting college papers, you can select a good topic for our list.

Impact of location, ethnicity, or age on vaccination rates
Uses of biomaterials in vaccination technology
Deafness: communication disorders
Household air pollution
Diabetes – a public danger
Coronaviruses
Oral health assessment
Tobacco and alcohol control
Diseases caused by lack of physical exercise
How urban pollution affects respiratory diseases
Healthy diets

Medicine Research Topics

Regardless of the requirements in your research assignment, you can write about something that is both engaging and useful in your future career. Choose a topic from below.

Causes for the increasing cancer cases
Insulin resistance
How terrorism affects mental health
AIDS/HIV – latest developments
Treating pregnant women versus non-pregnant women
Latest innovations in medical instruments
Genetic engineering
Successful treatment of mental diseases
Is autism a disease
Natural coma versus artificial coma
Treatments for sleep disorders and their effectiveness
Role of melatonin supplements in sleep quality

Healthcare Research Topics

Healthcare research includes political and social aspects, besides medical. For college students who want to explore how medicine is affected by society’s values or principles, we provide examples of topics for papers. Select yours from the list below.

Government investment in healthcare services in the EU versus the USA
Inequalities in healthcare assistance and services
Electronic health records systems – pros and cons
Can asylums treat mental issues
Health care for prison inmates
Equipment for improving the treatment of AIDS
Correlation between economic development and health care services across countries
Impact of smoking on organs
Heart attacks – causes and effects
Breast cancer – recent developments
Materials used in artificial tissue and their impacts

Public Health Research Topics

For current examples of public health topics, browse our list. We provide only original, researchable examples for which you can easily find supporting data and evidence.

Public versus private hospitals
Health Disparities in Diabetes Management Across Different Socioeconomic Groups
Health care professionals – management principles
Surgery failures – who is responsible
What legal responsibilities has the hospital administration
Patient service quality in public versus private hospitals
What benefits do national health care systems have
Estimated costs of cancer treatments
Public health in developing countries
Banning tobacco ads – importance for public health
Government solutions to the anti-vaccine’s movement
How the COVID-19 pandemic has changed public health regulations

Mental Health Research Paper Topics

Mental health is one of the most complex areas of medicine, where things are never as clear as with other medical issues. This increases the research potential of the field with plenty of topics left for debate.

Mental Health Impact of Social Media on American Teenagers
Causes of anxiety disorders
Bulimia versus anorexia
Childhood trauma
Mental health public policies
Impact of Lifestyle Factors on the Progression of Dementia in the Elderly Population
Postpartum Depression
Posttraumatic Stress Disorder
Seasonal Affective Disorder
Schizophrenia
Stress and its effects on sleep quality
Insomnia and its relation to mental health disorders

Anatomy Research Topics

Anatomy covers everything about the human body and how it works. If you find that intriguing and want to pay for medical research paper, start by selecting a topic.

Causes and treatments of virus infections
Chemotherapy: how it affects the body
Thyroid glands – functions in the body
Human endocrine system
Preventative Measures and Treatments for Common Liver Diseases
Heart diseases
How does the human muscular system develop
Lymphatic system – importance
Investigating genetic diseases
Digestive system
Role of the Spleen in the Human Immune System and Related Disorders

Biomedical Research Topics

Biology and medicine often work together. For the newest changes in the biomedical field, check our topics.

Comparative Efficacy of Alternative Medicine Practices in Chronic Pain Management
Alzheimer’s disease – paths for treatment
Vaccines and drug development in the treatment of Ebola
Antibiotic resistance
Biological effects caused by aging
Air pollution effects on health
Infectious disease past versus present
Regenerative medicine
Biomedical diagnostics
Biomedical technology
Advanced biomaterials for vaccine delivery

Bioethics Research Topics

A controversial area of medicine, bioethics is where you get the chance to add personal input to a research topic and come up with new insights. You could consider these subjects.

Organ donation
Alternative or complementary medicine
Assisted suicide or the right to die
Artificial insemination or surrogacy
Chemical and biological warfare
Contraception
Environmental bioethics
In Vitro Fertilization
Ethical considerations in medical research on animals

Cancer Research Topics

Are you writing a paper related to cancer causes, diagnosis, treatment or effects? Look below for a hot topic that it’s easy to research and important for medical advance.

The ability of immune system cells to fight cancer
Computational oncology
Metastasis affected by drug resistance
Stem cells – applications for cancer treatment
Tumor microenvironment
Obesity and age in cancer occurrence
Early cancer detection – benefits
Artificial intelligence predicting cancer
Hematologic malignancies
Pathogen-related cancers
Impact of COVID-19 on cancer treatment studies

Clinical Research Topics

Learn more about clinical medicine by conducting more in-depth research. We prepared for you a list of relevant issues to touch upon.

Ethical concerns regarding research on human subjects
Subject recruitment
Budget preparation
Human subject protection
Clinical trials – financial support
Clinical practices for health professionals
Using vulnerable populations in clinical research
Quality assurance in clinical research
Academic clinical trials versus clinical trials units
Data collection and management
Evolution of clinical symptoms in COVID-19 patients

Critical Care Research Topics

Critical care is a key area in medical studies. Explore these topics in your research paper to gain more valuable knowledge in this field. You can also get in contact with nursing research paper writers .

Obesity and asthma – clinical manifestations
Chronic obstructive pulmonary disease
Rhythm analysis for cardiac arrest
Traumatic brain injury – fluid resuscitation
Hydrocortisone for multiple trauma patients
Care and nutrition for critically ill adults
Diagnosis of hypersensitivity pneumonitis
Coma and sedation scales
Artificial airways suctioning
Arterial puncture and arterial line
Long-term cardiac and respiratory effects of COVID-19

Pediatric Research Topics

Any topic that refers to health care for children, pregnant women, mothers, and adolescents goes under pediatric care.

Early Intervention Methods for Children Diagnosed with Autism Spectrum Disorder
Preventive healthcare strategies for children
Impact of early childhood nutrition on long-term health
Attention deficit hyperactivity disorder (ADHD)
Congenital heart disease in newborns
Adolescent medicine
Neonatal medicine
Rare diseases in children and teenagers
Obesity and weight fluctuations
Behavioral sleep problems in children
Children with anemia
Child healthcare enhancements and innovations

Dental Research Topics Ideas

Choose a topic on oral health or dental care from this list of the most interesting topics in the field.

How smoking affects oral health
Children’s risk for dental caries
Causes of Dental Anxiety and Effective Interventions for Reducing Fear in Patients
Types of dental materials – new advances
Bad breath bacteria
How diabetes affects oral health
Oral cancer
Dental pain – types, causes
Dental implants
Oral health-related quality of life
Advancements in treatments for virus infections

Dermatology Research Topics

Find the best research topic for your dermatology paper among our examples.

Atopic dermatitis
Contact dermatitis
Epidemiology behind uncommon skin disorders
Cutaneous aging
Risk factors of melanoma skin cancer
Acne versus rosacea
Genetic testing for skin conditions
Effects of cosmetic agents on skin health
Improving skin barrier with pharmaceutical agents
Skin manifestations of autoimmune disorders
Study of virus effects on skin health

Primary Care Research Topics

Write a primary care paper that can demonstrate your research skills and interest in powerful scientific findings.

Primary care for vulnerable/uninsured populations
Interpersonal continuity in care treatment
How primary care contributes to health systems
Primary care delivery models
Developments in family medicine
Occupational/environmental health
Pharmacotherapy approaches
Formal allergy testing
Oral contraception side effects
Dietary or behavioral interventions for obesity management

Pharmaceutical Research Topics

Pharma students who need paper topics can use one from our list. We include all things related to pharmacy life.

Drugs that can treat cancer
Drug excretion
Elimination rate constant
Inflammatory stress drug treatment
Aspirin poising
Ibuprofen – dangers versus benefits
Toxicodynamics
Opioid use disorder
Pharmacotherapy for schizophrenia
Ketamine in depression treatment

Medical Anthropology Research Topics

Medical anthropology unites different areas of human knowledge. Find powerful ideas for a paper below.

Cultural contexts regarding reproductive health
Women sexuality
Anthropological aspects of health care
Contributions of social sciences to public health
Euthanasia and medical ethics across cultures
Health-related behavior in adults across cultures
Transcultural nursing
Forensic psychiatry
Symptoms of Celiac Disease – a disease with no symptoms
Nursing ethics

Paramedic Research Paper Topics

Topics for paramedic research must be based on evidence, data, statistics, or practical experience. Just like ours.

Trends and statistics in EMS
Disaster medicine
Mass casualties
Pandemics and epidemics
Infection control
Basic versus advanced life support
Scene safety in EMS
Shock management
Motor vehicle accidents
Challenges in medical humanitarian missions during pandemics

Surgery Research Topics

Discover all the intricacies of surgeries that save lives by writing about our topics.

Medical malpractice and legal issues
Methicillin-resistant Staphylococcus aureus
Early Detection and Management Strategies for Sepsis in Hospital Settings
Pain management
Perioperative nursing
Wound management
Colorectal cancer surgery
Breast cancer surgery
Minimally invasive surgeries
Vascular disease
Changes in surgical practices during pandemics

Radiology Research Paper Topics

Find a radiology topic related to your academic interests to write a successful paper.

Using MRI to diagnose hepatic focal lesions
Multidetector computer tomography
Ultrasound elastography in breast cancer
Assessing traumatic spinal cord injuries with MRI diffusion tensor imaging
Sonographic imaging to detect male infertility
Role of tomography in diagnosing cancer
Brain tumor surgery with magnetic resonance imaging
Bacterial meningitis imaging
Advanced imaging techniques for virus infection detection

Anatomy and Physiology Research Paper Topics

Any ideas for a medical research paper? We have included the most important topics for an anatomy and physiology paper.

What role has the endocrine system
Staphylococcus aureus
Environmental factors that affect development of human muscular system
What role has the lymphatic system
An investigation of genetic diseases
Explaining the aging process
The digestive tract
Effects of stress on cells and muscles
Evolution of the human nervous system
What role has the cardiovascular system
Impact of viruses on respiratory health in urban settings

Healthcare Management Research Paper Topics

There are numerous topics you could write about when it comes to healthcare management. There’s a wide range of options to pick, from infrastructure, staff, and financial management to HR and patient management. Here are some of the top healthcare management research paper options.

Medical Ethics Research Paper Topics

Medical ethics is a field that opens the door to numerous compelling topics for research papers. Here are some of the most appealing ones you could tackle.

Clinical research on humans
Vaccines and immunization
Religious beliefs in healthcare
Euthanasia and physician-assisted suicide
Ethical issues across cultures
Amniocentesis or prenatal birth defect testing
Medical malpractice and going back to work
Racial and ethnic preferences and perceptions in organ donations
Racial and ethnic disparities in healthcare
Ethical concerns of AI in healthcare
Debates on animal ethics in medical research
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Environmental Pollutants and Respiratory Health in Urban Areas of the USA
How environmental changes affect human health
Long-Term Impact of PM2.5 Exposure on Lung, Heart, and Brain Function
Health Risks of Air Pollution Across Different Life Stages
Hospital Admissions and Air Quality in the USA
Risk Reduction Strategies for Indoor Air Pollution from Gas Stoves
Impact of Air Pollution on Cognitive Development and Socioeconomic Achievements
Long-Term Health Effects of Early Childhood Exposure to Air Pollution
Impact of Traffic Noise on Cardiovascular Health

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Research Topics & Ideas: Healthcare

F inding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a healthcare-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of healthcare-related research ideas and topic thought-starters across a range of healthcare fields, including allopathic and alternative medicine, dentistry, physical therapy, optometry, pharmacology and public health.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the healthcare domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: Healthcare Research Topics

Allopathic medicine
Alternative /complementary medicine
Veterinary medicine
Physical therapy/ rehab
Optometry and ophthalmology
Pharmacy and pharmacology
Public health
Examples of healthcare-related dissertations

Allopathic (Conventional) Medicine

The effectiveness of telemedicine in remote elderly patient care
The impact of stress on the immune system of cancer patients
The effects of a plant-based diet on chronic diseases such as diabetes
The use of AI in early cancer diagnosis and treatment
The role of the gut microbiome in mental health conditions such as depression and anxiety
The efficacy of mindfulness meditation in reducing chronic pain: A systematic review
The benefits and drawbacks of electronic health records in a developing country
The effects of environmental pollution on breast milk quality
The use of personalized medicine in treating genetic disorders
The impact of social determinants of health on chronic diseases in Asia
The role of high-intensity interval training in improving cardiovascular health
The efficacy of using probiotics for gut health in pregnant women
The impact of poor sleep on the treatment of chronic illnesses
The role of inflammation in the development of chronic diseases such as lupus
The effectiveness of physiotherapy in pain control post-surgery

Topics & Ideas: Alternative Medicine

The benefits of herbal medicine in treating young asthma patients
The use of acupuncture in treating infertility in women over 40 years of age
The effectiveness of homoeopathy in treating mental health disorders: A systematic review
The role of aromatherapy in reducing stress and anxiety post-surgery
The impact of mindfulness meditation on reducing high blood pressure
The use of chiropractic therapy in treating back pain of pregnant women
The efficacy of traditional Chinese medicine such as Shun-Qi-Tong-Xie (SQTX) in treating digestive disorders in China
The impact of yoga on physical and mental health in adolescents
The benefits of hydrotherapy in treating musculoskeletal disorders such as tendinitis
The role of Reiki in promoting healing and relaxation post birth
The effectiveness of naturopathy in treating skin conditions such as eczema
The use of deep tissue massage therapy in reducing chronic pain in amputees
The impact of tai chi on the treatment of anxiety and depression
The benefits of reflexology in treating stress, anxiety and chronic fatigue
The role of acupuncture in the prophylactic management of headaches and migraines

Topics & Ideas: Dentistry

The impact of sugar consumption on the oral health of infants
The use of digital dentistry in improving patient care: A systematic review
The efficacy of orthodontic treatments in correcting bite problems in adults
The role of dental hygiene in preventing gum disease in patients with dental bridges
The impact of smoking on oral health and tobacco cessation support from UK dentists
The benefits of dental implants in restoring missing teeth in adolescents
The use of lasers in dental procedures such as root canals
The efficacy of root canal treatment using high-frequency electric pulses in saving infected teeth
The role of fluoride in promoting remineralization and slowing down demineralization
The impact of stress-induced reflux on oral health
The benefits of dental crowns in restoring damaged teeth in elderly patients
The use of sedation dentistry in managing dental anxiety in children
The efficacy of teeth whitening treatments in improving dental aesthetics in patients with braces
The role of orthodontic appliances in improving well-being
The impact of periodontal disease on overall health and chronic illnesses

Free Webinar: How To Find A Dissertation Research Topic

Topics & Ideas: Veterinary Medicine

The impact of nutrition on broiler chicken production
The role of vaccines in disease prevention in horses
The importance of parasite control in animal health in piggeries
The impact of animal behaviour on welfare in the dairy industry
The effects of environmental pollution on the health of cattle
The role of veterinary technology such as MRI in animal care
The importance of pain management in post-surgery health outcomes
The impact of genetics on animal health and disease in layer chickens
The effectiveness of alternative therapies in veterinary medicine: A systematic review
The role of veterinary medicine in public health: A case study of the COVID-19 pandemic
The impact of climate change on animal health and infectious diseases in animals
The importance of animal welfare in veterinary medicine and sustainable agriculture
The effects of the human-animal bond on canine health
The role of veterinary medicine in conservation efforts: A case study of Rhinoceros poaching in Africa
The impact of veterinary research of new vaccines on animal health

Topics & Ideas: Physical Therapy/Rehab

The efficacy of aquatic therapy in improving joint mobility and strength in polio patients
The impact of telerehabilitation on patient outcomes in Germany
The effect of kinesiotaping on reducing knee pain and improving function in individuals with chronic pain
A comparison of manual therapy and yoga exercise therapy in the management of low back pain
The use of wearable technology in physical rehabilitation and the impact on patient adherence to a rehabilitation plan
The impact of mindfulness-based interventions in physical therapy in adolescents
The effects of resistance training on individuals with Parkinson’s disease
The role of hydrotherapy in the management of fibromyalgia
The impact of cognitive-behavioural therapy in physical rehabilitation for individuals with chronic pain
The use of virtual reality in physical rehabilitation of sports injuries
The effects of electrical stimulation on muscle function and strength in athletes
The role of physical therapy in the management of stroke recovery: A systematic review
The impact of pilates on mental health in individuals with depression
The use of thermal modalities in physical therapy and its effectiveness in reducing pain and inflammation
The effect of strength training on balance and gait in elderly patients

Need a helping hand?

descriptive research topics in medical technology

Topics & Ideas: Optometry & Opthalmology

The impact of screen time on the vision and ocular health of children under the age of 5
The effects of blue light exposure from digital devices on ocular health
The role of dietary interventions, such as the intake of whole grains, in the management of age-related macular degeneration
The use of telemedicine in optometry and ophthalmology in the UK
The impact of myopia control interventions on African American children’s vision
The use of contact lenses in the management of dry eye syndrome: different treatment options
The effects of visual rehabilitation in individuals with traumatic brain injury
The role of low vision rehabilitation in individuals with age-related vision loss: challenges and solutions
The impact of environmental air pollution on ocular health
The effectiveness of orthokeratology in myopia control compared to contact lenses
The role of dietary supplements, such as omega-3 fatty acids, in ocular health
The effects of ultraviolet radiation exposure from tanning beds on ocular health
The impact of computer vision syndrome on long-term visual function
The use of novel diagnostic tools in optometry and ophthalmology in developing countries
The effects of virtual reality on visual perception and ocular health: an examination of dry eye syndrome and neurologic symptoms

Topics & Ideas: Pharmacy & Pharmacology

The impact of medication adherence on patient outcomes in cystic fibrosis
The use of personalized medicine in the management of chronic diseases such as Alzheimer’s disease
The effects of pharmacogenomics on drug response and toxicity in cancer patients
The role of pharmacists in the management of chronic pain in primary care
The impact of drug-drug interactions on patient mental health outcomes
The use of telepharmacy in healthcare: Present status and future potential
The effects of herbal and dietary supplements on drug efficacy and toxicity
The role of pharmacists in the management of type 1 diabetes
The impact of medication errors on patient outcomes and satisfaction
The use of technology in medication management in the USA
The effects of smoking on drug metabolism and pharmacokinetics: A case study of clozapine
Leveraging the role of pharmacists in preventing and managing opioid use disorder
The impact of the opioid epidemic on public health in a developing country
The use of biosimilars in the management of the skin condition psoriasis
The effects of the Affordable Care Act on medication utilization and patient outcomes in African Americans

Topics & Ideas: Public Health

The impact of the built environment and urbanisation on physical activity and obesity
The effects of food insecurity on health outcomes in Zimbabwe
The role of community-based participatory research in addressing health disparities
The impact of social determinants of health, such as racism, on population health
The effects of heat waves on public health
The role of telehealth in addressing healthcare access and equity in South America
The impact of gun violence on public health in South Africa
The effects of chlorofluorocarbons air pollution on respiratory health
The role of public health interventions in reducing health disparities in the USA
The impact of the United States Affordable Care Act on access to healthcare and health outcomes
The effects of water insecurity on health outcomes in the Middle East
The role of community health workers in addressing healthcare access and equity in low-income countries
The impact of mass incarceration on public health and behavioural health of a community
The effects of floods on public health and healthcare systems
The role of social media in public health communication and behaviour change in adolescents

Examples: Healthcare Dissertation & Theses

While the ideas we’ve presented above are a decent starting point for finding a healthcare-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various healthcare-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

Improving Follow-Up Care for Homeless Populations in North County San Diego (Sanchez, 2021)
On the Incentives of Medicare’s Hospital Reimbursement and an Examination of Exchangeability (Elzinga, 2016)
Managing the healthcare crisis: the career narratives of nurses (Krueger, 2021)
Methods for preventing central line-associated bloodstream infection in pediatric haematology-oncology patients: A systematic literature review (Balkan, 2020)
Farms in Healthcare: Enhancing Knowledge, Sharing, and Collaboration (Garramone, 2019)
When machine learning meets healthcare: towards knowledge incorporation in multimodal healthcare analytics (Yuan, 2020)
Integrated behavioural healthcare: The future of rural mental health (Fox, 2019)
Healthcare service use patterns among autistic adults: A systematic review with narrative synthesis (Gilmore, 2021)
Mindfulness-Based Interventions: Combatting Burnout and Compassionate Fatigue among Mental Health Caregivers (Lundquist, 2022)
Transgender and gender-diverse people’s perceptions of gender-inclusive healthcare access and associated hope for the future (Wille, 2021)
Efficient Neural Network Synthesis and Its Application in Smart Healthcare (Hassantabar, 2022)
The Experience of Female Veterans and Health-Seeking Behaviors (Switzer, 2022)
Machine learning applications towards risk prediction and cost forecasting in healthcare (Singh, 2022)
Does Variation in the Nursing Home Inspection Process Explain Disparity in Regulatory Outcomes? (Fox, 2020)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

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18 Comments

I need topics that will match the Msc program am running in healthcare research please

Hello Mabel,

I can help you with a good topic, kindly provide your email let’s have a good discussion on this.

Can you provide some research topics and ideas on Immunology?

Thank you to create new knowledge on research problem verse research topic

Help on problem statement on teen pregnancy

This post might be useful: https://gradcoach.com/research-problem-statement/

can you give me research titles that i can conduct as a school nurse

can you provide me with a research topic on healthcare related topics to a qqi level 5 student

Please can someone help me with research topics in public health ?

Hello I have requirement of Health related latest research issue/topics for my social media speeches. If possible pls share health issues , diagnosis, treatment.

I would like a topic thought around first-line support for Gender-Based Violence for survivors or one related to prevention of Gender-Based Violence

Please can I be helped with a master’s research topic in either chemical pathology or hematology or immunology? thanks

Can u please provide me with a research topic on occupational health and safety at the health sector

Good day kindly help provide me with Ph.D. Public health topics on Reproductive and Maternal Health, interventional studies on Health Education

may you assist me with a good easy healthcare administration study topic

May you assist me in finding a research topic on nutrition,physical activity and obesity. On the impact on children

I have been racking my brain for a while on what topic will be suitable for my PhD in health informatics. I want a qualitative topic as this is my strong area.

Hi, may I please be assisted with research topics in the medical laboratory sciences

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Research article
Open access
Published: 10 April 2021

The role of artificial intelligence in healthcare: a structured literature review

Silvana Secinaro 1 ,
Davide Calandra 1 ,
Aurelio Secinaro 2 ,
Vivek Muthurangu 3 &
Paolo Biancone 1

BMC Medical Informatics and Decision Making volume 21 , Article number: 125 ( 2021 ) Cite this article

161k Accesses

321 Citations

28 Altmetric

Metrics details

Background/Introduction

Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions.

The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package.

The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths.

Conclusions

The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Peer Review reports

Artificial intelligence (AI) generally applies to computational technologies that emulate mechanisms assisted by human intelligence, such as thought, deep learning, adaptation, engagement, and sensory understanding [ 1 , 2 ]. Some devices can execute a role that typically involves human interpretation and decision-making [ 3 , 4 ]. These techniques have an interdisciplinary approach and can be applied to different fields, such as medicine and health. AI has been involved in medicine since as early as the 1950s, when physicians made the first attempts to improve their diagnoses using computer-aided programs [ 5 , 6 ]. Interest and advances in medical AI applications have surged in recent years due to the substantially enhanced computing power of modern computers and the vast amount of digital data available for collection and utilisation [ 7 ]. AI is gradually changing medical practice. There are several AI applications in medicine that can be used in a variety of medical fields, such as clinical, diagnostic, rehabilitative, surgical, and predictive practices. Another critical area of medicine where AI is making an impact is clinical decision-making and disease diagnosis. AI technologies can ingest, analyse, and report large volumes of data across different modalities to detect disease and guide clinical decisions [ 3 , 8 ]. AI applications can deal with the vast amount of data produced in medicine and find new information that would otherwise remain hidden in the mass of medical big data [ 9 , 10 , 11 ]. These technologies can also identify new drugs for health services management and patient care treatments [ 5 , 6 ].

Courage in the application of AI is visible through a search in the primary research databases. However, as Meskò et al. [ 7 ] find, the technology will potentially reduce care costs and repetitive operations by focusing the medical profession on critical thinking and clinical creativity. As Cho et al. and Doyle et al. [ 8 , 9 ] add, the AI perspective is exciting; however, new studies will be needed to establish the efficacy and applications of AI in the medical field [ 10 ].

Our paper will also concentrate on AI strategies for healthcare from the accounting, business, and management perspectives. The authors used the structured literature review (SLR) method for its reliable and replicable research protocol [ 11 ] and selected bibliometric variables as sources of investigation. Bibliometric usage enables the recognition of the main quantitative variables of the study stream [ 12 ]. This method facilitates the detection of the required details of a particular research subject, including field authors, number of publications, keywords for interaction between variables (policies, properties and governance) and country data [ 13 ]. It also allows the application of the science mapping technique [ 14 ]. Our paper adopted the Bibliometrix R package and the biblioshiny web interface as tools of analysis [ 14 ].

The investigation offers the following insights for future researchers and practitioners:

bibliometric information on 288 peer-reviewed English papers from the Scopus collection.

Identification of leading journals in this field, such as Journal of Medical Systems, Studies in Health Technology and Informatics, IEEE Journal of Biomedical and Health Informatics, and Decision Support Systems.

Qualitative and quantitative information on authors’ Lotka’s law, h-index, g-index, m-index, keyword, and citation data.

Research on specific countries to assess AI in the delivery and effectiveness of healthcare, quotes, and networks within each region.

A topic dendrogram study that identifies five research clusters: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making.

An in-depth discussion that develops theoretical and practical implications for future studies.

The paper is organised as follows. Section 2 lists the main bibliometric articles in this field. Section 3 elaborates on the methodology. Section 4 presents the findings of the bibliometric analysis. Section 5 discusses the main elements of AI in healthcare based on the study results. Section 6 concludes the article with future implications for research.

Related works and originality

As suggested by Zupic and Čater [ 15 ], a research stream can be evaluated with bibliometric methods that can introduce objectivity and mitigate researcher bias. For this reason, bibliometric methods are attracting increasing interest among researchers as a reliable and impersonal research analytical approach [ 16 , 17 ]. Recently, bibliometrics has been an essential method for analysing and predicting research trends [ 18 ]. Table 1 lists other research that has used a similar approach in the research stream investigated.

The scientific articles reported show substantial differences in keywords and research topics that have been previously studied. The bibliometric analysis of Huang et al. [ 19 ] describes rehabilitative medicine using virtual reality technology. According to the authors, the primary goal of rehabilitation is to enhance and restore functional ability and quality of life for patients with physical impairments or disabilities. In recent years, many healthcare disciplines have been privileged to access various technologies that provide tools for both research and clinical intervention.

Hao et al. [ 20 ] focus on text mining in medical research. As reported, text mining reveals new, previously unknown information by using a computer to automatically extract information from different text resources. Text mining methods can be regarded as an extension of data mining to text data. Text mining is playing an increasingly significant role in processing medical information. Similarly, the studies by dos Santos et al. [ 21 ] focus on applying data mining and machine learning (ML) techniques to public health problems. As stated in this research, public health may be defined as the art and science of preventing diseases, promoting health, and prolonging life. Using data mining and ML techniques, it is possible to discover new information that otherwise would be hidden. These two studies are related to another topic: medical big data. According to Liao et al. [ 22 ], big data is a typical “buzzword” in the business and research community, referring to a great mass of digital data collected from various sources. In the medical field, we can obtain a vast amount of data (i.e., medical big data). Data mining and ML techniques can help deal with this information and provide helpful insights for physicians and patients. More recently, Choudhury et al. [ 23 ] provide a systematic review on the use of ML to improve the care of elderly patients, demonstrating eligible studies primarily in psychological disorders and eye diseases.

Tran et al. [ 2 ] focus on the global evolution of AI research in medicine. Their bibliometric analysis highlights trends and topics related to AI applications and techniques. As stated in Connelly et al.’s [ 24 ] study, robot-assisted surgeries have rapidly increased in recent years. Their bibliometric analysis demonstrates how robotic-assisted surgery has gained acceptance in different medical fields, such as urological, colorectal, cardiothoracic, orthopaedic, maxillofacial and neurosurgery applications. Additionally, the bibliometric analysis of Guo et al. [ 25 ] provides an in-depth study of AI publications through December 2019. The paper focuses on tangible AI health applications, giving researchers an idea of how algorithms can help doctors and nurses. A new stream of research related to AI is also emerging. In this sense, Choudhury and Asan’s [ 26 ] scientific contribution provides a systematic review of the AI literature to identify health risks for patients. They report on 53 studies involving technology for clinical alerts, clinical reports, and drug safety. Considering the considerable interest within this research stream, this analysis differs from the current literature for several reasons. It aims to provide in-depth discussion, considering mainly the business, management, and accounting fields and not dealing only with medical and health profession publications.

Additionally, our analysis aims to provide a bibliometric analysis of variables such as authors, countries, citations and keywords to guide future research perspectives for researchers and practitioners, as similar analyses have done for several publications in other research streams [ 15 , 16 , 27 ]. In doing so, we use a different database, Scopus, that is typically adopted in social sciences fields. Finally, our analysis will propose and discuss a dominant framework of variables in this field, and our analysis will not be limited to AI application descriptions.

Methodology

This paper evaluated AI in healthcare research streams using the SLR method [ 11 ]. As suggested by Massaro et al. [ 11 ], an SLR enables the study of the scientific corpus of a research field, including the scientific rigour, reliability and replicability of operations carried out by researchers. As suggested by many scholars, the methodology allows qualitative and quantitative variables to highlight the best authors, journals and keywords and combine a systematic literature review and bibliometric analysis [ 27 , 28 , 29 , 30 ]. Despite its widespread use in business and management [ 16 , 31 ], the SLR is also used in the health sector based on the same philosophy through which it was originally conceived [ 32 , 33 ]. A methodological analysis of previously published articles reveals that the most frequently used steps are as follows [ 28 , 31 , 34 ]:

defining research questions;

writing the research protocol;

defining the research sample to be analysed;

developing codes for analysis; and

critically analysing, discussing, and identifying a future research agenda.

Considering the above premises, the authors believe that an SLR is the best method because it combines scientific validity, replicability of the research protocol and connection between multiple inputs.

As stated by the methodological paper, the first step is research question identification. For this purpose, we benefit from the analysis of Zupic and Čater [ 15 ], who provide several research questions for future researchers to link the study of authors, journals, keywords and citations. Therefore, RQ1 is “What are the most prominent authors, journal keywords and citations in the field of the research study?” Additionally, as suggested by Haleem et al. [ 35 ], new technologies, including AI, are changing the medical field in unexpected timeframes, requiring studies in multiple areas. Therefore, RQ2 is “How does artificial intelligence relate to healthcare, and what is the focus of the literature?” Then, as discussed by Massaro et al. [ 36 ], RQ3 is “What are the research applications of artificial intelligence for healthcare?”.

The first research question aims to define the qualitative and quantitative variables of the knowledge flow under investigation. The second research question seeks to determine the state of the art and applications of AI in healthcare. Finally, the third research question aims to help researchers identify practical and theoretical implications and future research ideas in this field.

The second fundamental step of the SLR is writing the research protocol [ 11 ]. Table 2 indicates the currently known literature elements, uniquely identifying the research focus, motivations and research strategy adopted and the results providing a link with the following points. Additionally, to strengthen the analysis, our investigation benefits from the PRISMA statement methodological article [ 37 ]. Although the SLR is a validated method for systematic reviews and meta-analyses, we believe that the workflow provided may benefit the replicability of the results [ 37 , 38 , 39 , 40 ]. Figure 1 summarises the researchers’ research steps, indicating that there are no results that can be referred to as a meta-analysis.

Source : Authors’ elaboration on Liberati et al. [ 37 ]

PRISMA workflow.

The third step is to specify the search strategy and search database. Our analysis is based on the search string “Artificial Intelligence” OR “AI” AND “Healthcare” with a focus on “Business, Management, and Accounting”, “Decision Sciences”, and “Health professions”. As suggested by [ 11 , 41 ] and motivated by [ 42 ], keywords can be selected through a top-down approach by identifying a large search field and then focusing on particular sub-topics. The paper uses data retrieved from the Scopus database, a multi-disciplinary database, which allowed the researchers to identify critical articles for scientific analysis [ 43 ]. Additionally, Scopus was selected based on Guo et al.’s [ 25 ] limitations, which suggest that “future studies will apply other databases, such as Scopus, to explore more potential papers” . The research focuses on articles and reviews published in peer-reviewed journals for their scientific relevance [ 11 , 16 , 17 , 29 ] and does not include the grey literature, conference proceedings or books/book chapters. Articles written in any language other than English were excluded [ 2 ]. For transparency and replicability, the analysis was conducted on 11 January 2021. Using this research strategy, the authors retrieved 288 articles. To strengthen the study's reliability, we publicly provide the full bibliometric extract on the Zenodo repository [ 44 , 45 ].

The fourth research phase is defining the code framework that initiates the analysis of the variables. The study will identify the following:

descriptive information of the research area;

source analysis [ 16 ];

author and citation analysis [ 28 ];

keywords and network analysis [ 14 ]; and

geographic distribution of the papers [ 14 ].

The final research phase is the article’s discussion and conclusion, where implications and future research trends will be identified.

At the research team level, the information is analysed with the statistical software R-Studio and the Bibliometrix package [ 15 ], which allows scientific analysis of the results obtained through the multi-disciplinary database.

The analysis of bibliometric results starts with a description of the main bibliometric statistics with the aim of answering RQ1, What are the most prominent authors, journal keywords and citations in the field of the research study?, and RQ2, How does artificial intelligence relate to healthcare, and what is the focus of the literature? Therefore, the following elements were thoroughly analysed: (1) type of document; (2) annual scientific production; (3) scientific sources; (4) source growth; (5) number of articles per author; (6) author’s dominance ranking; (7) author’s h-index, g-index, and m-index; (8) author’s productivity; (9) author’s keywords; (10) topic dendrogram; (11) a factorial map of the document with the highest contributions; (12) article citations; (13) country production; (14) country citations; (15) country collaboration map; and (16) country collaboration network.

Main information

Table 3 shows the information on 288 peer-reviewed articles published between 1992 and January 2021 extracted from the Scopus database. The number of keywords is 946 from 136 sources, and the number of keywords plus, referring to the number of keywords that frequently appear in an article’s title, was 2329. The analysis period covered 28 years and 1 month of scientific production and included an annual growth rate of 5.12%. However, the most significant increase in published articles occurred in the past three years (please see Fig. 2 ). On average, each article was written by three authors (3.56). Finally, the collaboration index (CI), which was calculated as the total number of authors of multi-authored articles/total number of multi-authored articles, was 3.97 [ 46 ].

Source : Authors’ elaboration

Annual scientific production.

Table 4 shows the top 20 sources related to the topic. The Journal of Medical Systems is the most relevant source, with twenty-one of the published articles. This journal's main issues are the foundations, functionality, interfaces, implementation, impacts, and evaluation of medical technologies. Another relevant source is Studies in Health Technology and Informatics, with eleven articles. This journal aims to extend scientific knowledge related to biomedical technologies and medical informatics research. Both journals deal with cloud computing, machine learning, and AI as a disruptive healthcare paradigm based on recent publications. The IEEE Journal of Biomedical and Health Informatics investigates technologies in health care, life sciences, and biomedicine applications from a broad perspective. The next journal, Decision Support Systems, aims to analyse how these technologies support decision-making from a multi-disciplinary view, considering business and management. Therefore, the analysis of the journals revealed that we are dealing with an interdisciplinary research field. This conclusion is confirmed, for example, by the presence of purely medical journals, journals dedicated to the technological growth of healthcare, and journals with a long-term perspective such as futures.

The distribution frequency of the articles (Fig. 3 ) indicates the journals dealing with the topic and related issues. Between 2008 and 2012, a significant growth in the number of publications on the subject is noticeable. However, the graph shows the results of the Loess regression, which includes the quantity and publication time of the journal under analysis as variables. This method allows the function to assume an unlimited distribution; that is, feature can consider values below zero if the data are close to zero. It contributes to a better visual result and highlights the discontinuity in the publication periods [ 47 ].

Source growth. Source : Authors’ elaboration

Finally, Fig. 4 provides an analytical perspective on factor analysis for the most cited papers. As indicated in the literature [ 48 , 49 ], using factor analysis to discover the most cited papers allows for a better understanding of the scientific world’s intellectual structure. For example, our research makes it possible to consider certain publications that effectively analyse subject specialisation. For instance, Santosh’s [ 50 ] article addresses the new paradigm of AI with ML algorithms for data analysis and decision support in the COVID-19 period, setting a benchmark in terms of citations by researchers. Moving on to the application, an article by Shickel et al. [ 51 ] begins with the belief that the healthcare world currently has much health and administrative data. In this context, AI and deep learning will support medical and administrative staff in extracting data, predicting outcomes, and learning medical representations. Finally, in the same line of research, Baig et al. [ 52 ], with a focus on wearable patient monitoring systems (WPMs), conclude that AI and deep learning may be landmarks for continuous patient monitoring and support for healthcare delivery.

Factorial map of the most cited documents.

This section identifies the most cited authors of articles on AI in healthcare. It also identifies the authors’ keywords, dominance factor (DF) ranking, h-index, productivity, and total number of citations. Table 5 identifies the authors and their publications in the top 20 rankings. As the table shows, Bushko R.G. has the highest number of publications: four papers. He is the editor-in-chief of Future of Health Technology, a scientific journal that aims to develop a clear vision of the future of health technology. Then, several authors each wrote three papers. For instance, Liu C. is a researcher active in the topic of ML and computer vision, and Sharma A. from Emory University Atlanta in the USA is a researcher with a clear focus on imaging and translational informatics. Some other authors have two publications each. While some authors have published as primary authors, most have published as co-authors. Hence, in the next section, we measure the contributory power of each author by investigating the DF ranking through the number of elements.

Authors’ dominance ranking

The dominance factor (DF) is a ratio measuring the fraction of multi-authored articles in which an author acts as the first author [ 53 ]. Several bibliometric studies use the DF in their analyses [ 46 , 54 ]. The DF ranking calculates an author’s dominance in producing articles. The DF is calculated by dividing the number of an author’s multi-authored papers as the first author (Nmf) by the author's total number of multi-authored papers (Nmt). This is omitted in the single-author case due to the constant value of 1 for single-authored articles. This formulation could lead to some distortions in the results, especially in fields where the first author is entered by surname alphabetical order [ 55 ].

The mathematical equation for the DF is shown as:

Table 6 lists the top 20 DF rankings. The data in the table show a low level of articles per author, either for first-authored or multi-authored articles. The results demonstrate that we are dealing with an emerging topic in the literature. Additionally, as shown in the table, Fox J. and Longoni C. are the most dominant authors in the field.

Authors’ impact

Table 7 shows the impact of authors in terms of the h-index [ 56 ] (i.e., the productivity and impact of citations of a researcher), g-index [ 57 ] (i.e., the distribution of citations received by a researcher's publications), m-index [ 58 ] (i.e., the h-index value per year), total citations, total paper and years of scientific publication. The H-index was introduced in the literature as a metric for the objective comparison of scientific results and depended on the number of publications and their impact [ 59 ]. The results show that the 20 most relevant authors have an h-index between 2 and 1. For the practical interpretation of the data, the authors considered data published by the London School of Economics [ 60 ]. In the social sciences, the analysis shows values of 7.6 for economic publications by professors and researchers who had been active for several years. Therefore, the youthfulness of the research area has attracted young researchers and professors. At the same time, new indicators have emerged over the years to diversify the logic of the h-index. For example, the g-index indicates an author's impact on citations, considering that a single article can generate these. The m-index, on the other hand, shows the cumulative value over the years.

The analysis, also considering the total number of citations, the number of papers published and the year of starting to publish, thus confirms that we are facing an expanding research flow.

Authors’ productivity

Figure 5 shows Lotka’s law. This mathematical formulation originated in 1926 to describe the publication frequency by authors in a specific research field [ 61 ]. In practice, the law states that the number of authors contributing to research in a given period is a fraction of the number who make up a single contribution [ 14 , 61 ].

Lotka’s law.

The mathematical relationship is expressed in reverse in the following way:

where y x is equal to the number of authors producing x articles in each research field. Therefore, C and n are constants that can be estimated in the calculation.

The figure's results are in line with Lotka's results, with an average of two publications per author in a given research field. In addition, the figure shows the percentage of authors. Our results lead us to state that we are dealing with a young and growing research field, even with this analysis. Approximately 70% of the authors had published only their first research article. Only approximately 20% had published two scientific papers.

Authors’ keywords

This section provides information on the relationship between the keywords artificial intelligence and healthcare . This analysis is essential to determine the research trend, identify gaps in the discussion on AI in healthcare, and identify the fields that can be interesting as research areas [ 42 , 62 ].

Table 8 highlights the total number of keywords per author in the top 20 positions. The ranking is based on the following elements: healthcare, artificial intelligence, and clinical decision support system . Keyword analysis confirms the scientific area of reference. In particular, we deduce the definition as “Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” [ 2 , 63 ]. Panch et al. [ 4 ] find that these technologies can be used in different business and management areas. After the first keyword, the analysis reveals AI applications and related research such as machine learning and deep learning.

Additionally, data mining and big data are a step forward in implementing exciting AI applications. According to our specific interest, if we applied AI in healthcare, we would achieve technological applications to help and support doctors and medical researchers in decision-making. The link between AI and decision-making is the reason why we find, in the seventh position, the keyword clinical decision support system . AI techniques can unlock clinically relevant information hidden in the massive amount of data that can assist clinical decision-making [ 64 ]. If we analyse the following keywords, we find other elements related to decision-making and support systems.

The TreeMap below (Fig. 6 ) highlights the combination of possible keywords representing AI and healthcare.

Keywords treemap.

The topic dendrogram in Fig. 7 represents the hierarchical order and the relationship between the keywords generated by hierarchical clustering [ 42 ]. The cut in the figure and the vertical lines facilitate an investigation and interpretation of the different clusters. As stated by Andrews [ 48 ], the figure is not intended to find the perfect level of associations between clusters. However, it aims to estimate the approximate number of clusters to facilitate further discussion.

Topic dendrogram.

The research stream of AI in healthcare is divided into two main strands. The blue strand focuses on medical information systems and the internet. Some papers are related to healthcare organisations, such as the Internet of Things, meaning that healthcare organisations use AI to support health services management and data analysis. AI applications are also used to improve diagnostic and therapeutic accuracy and the overall clinical treatment process [ 2 ]. If we consider the second block, the red one, three different clusters highlight separate aspects of the topic. The first could be explained as AI and ML predictive algorithms. Through AI applications, it is possible to obtain a predictive approach that can ensure that patients are better monitored. This also allows a better understanding of risk perception for doctors and medical researchers. In the second cluster, the most frequent words are decisions , information system , and support system . This means that AI applications can support doctors and medical researchers in decision-making. Information coming from AI technologies can be used to consider difficult problems and support a more straightforward and rapid decision-making process. In the third cluster, it is vital to highlight that the ML model can deal with vast amounts of data. From those inputs, it can return outcomes that can optimise the work of healthcare organisations and scheduling of medical activities.

Furthermore, the word cloud in Fig. 8 highlights aspects of AI in healthcare, such as decision support systems, decision-making, health services management, learning systems, ML techniques and diseases. The figure depicts how AI is linked to healthcare and how it is used in medicine.

Word cloud.

Figure 9 represents the search trends based on the keywords analysed. The research started in 2012. First, it identified research topics related to clinical decision support systems. This topic was recurrent during the following years. Interestingly, in 2018, studies investigated AI and natural language processes as possible tools to manage patients and administrative elements. Finally, a new research stream considers AI's role in fighting COVID-19 [ 65 , 66 ].

Keywords frequency.

Table 9 represents the number of citations from other articles within the top 20 rankings. The analysis allows the benchmark studies in the field to be identified [ 48 ]. For instance, Burke et al. [ 67 ] writes the most cited paper and analyses efficient nurse rostering methodologies. The paper critically evaluates tangible interdisciplinary solutions that also include AI. Immediately thereafter, Ahmed M.A.'s article proposes a data-driven optimisation methodology to determine the optimal number of healthcare staff to optimise patients' productivity [ 68 ]. Finally, the third most cited article lays the groundwork for developing deep learning by considering diverse health and administrative information [ 51 ].

This section analyses the diffusion of AI in healthcare around the world. It highlights countries to show the geographies of this research. It includes all published articles, the total number of citations, and the collaboration network. The following sub-sections start with an analysis of the total number of published articles.

Country total articles

Figure 9 and Table 10 display the countries where AI in healthcare has been considered. The USA tops the list of countries with the maximum number of articles on the topic (215). It is followed by China (83), the UK (54), India (51), Australia (54), and Canada (32). It is immediately evident that the theme has developed on different continents, highlighting a growing interest in AI in healthcare. The figure shows that many areas, such as Russia, Eastern Europe and Africa except for Algeria, Egypt, and Morocco, have still not engaged in this scientific debate.

Country publications and collaboration map

This section discusses articles on AI in healthcare in terms of single or multiple publications in each country. It also aims to observe collaboration and networking between countries. Table 11 and Fig. 10 highlight the average citations by state and show that the UK, the USA, and Kuwait have a higher average number of citations than other countries. Italy, Spain and New Zealand have the most significant number of citations.

Articles per country.

Figure 11 depicts global collaborations. The blue colour on the map represents research cooperation among nations. Additionally, the pink border linking states indicates the extent of collaboration between authors. The primary cooperation between nations is between the USA and China, with two collaborative articles. Other collaborations among nations are limited to a few papers.

Collaboration map.

Artificial intelligence for healthcare: applications

This section aims to strengthen the research scope by answering RQ3: What are the research applications of artificial intelligence for healthcare?

Benefiting from the topical dendrogram, researchers will provide a development model based on four relevant variables [ 69 , 70 ]. AI has been a disruptive innovation in healthcare [ 4 ]. With its sophisticated algorithms and several applications, AI has assisted doctors and medical professionals in the domains of health information systems, geocoding health data, epidemic and syndromic surveillance, predictive modelling and decision support, and medical imaging [ 2 , 9 , 10 , 64 ]. Furthermore, the researchers considered the bibliometric analysis to identify four macro-variables dominant in the field and used them as authors' keywords. Therefore, the following sub-sections aim to explain the debate on applications in healthcare for AI techniques. These elements are shown in Fig. 12 .

Dominant variables for AI in healthcare.

Health services management

One of the notable aspects of AI techniques is potential support for comprehensive health services management. These applications can support doctors, nurses and administrators in their work. For instance, an AI system can provide health professionals with constant, possibly real-time medical information updates from various sources, including journals, textbooks, and clinical practices [ 2 , 10 ]. These applications' strength is becoming even more critical in the COVID-19 period, during which information exchange is continually needed to properly manage the pandemic worldwide [ 71 ]. Other applications involve coordinating information tools for patients and enabling appropriate inferences for health risk alerts and health outcome prediction [ 72 ]. AI applications allow, for example, hospitals and all health services to work more efficiently for the following reasons:

Clinicians can access data immediately when they need it.

Nurses can ensure better patient safety while administering medication.

Patients can stay informed and engaged in their care by communicating with their medical teams during hospital stays.

Additionally, AI can contribute to optimising logistics processes, for instance, realising drugs and equipment in a just-in-time supply system based totally on predictive algorithms [ 73 , 74 ]. Interesting applications can also support the training of personnel working in health services. This evidence could be helpful in bridging the gap between urban and rural health services [ 75 ]. Finally, health services management could benefit from AI to leverage the multiplicity of data in electronic health records by predicting data heterogeneity across hospitals and outpatient clinics, checking for outliers, performing clinical tests on the data, unifying patient representation, improving future models that can predict diagnostic tests and analyses, and creating transparency with benchmark data for analysing services delivered [ 51 , 76 ].

Predictive medicine

Another relevant topic is AI applications for disease prediction and diagnosis treatment, outcome prediction and prognosis evaluation [ 72 , 77 ]. Because AI can identify meaningful relationships in raw data, it can support diagnostic, treatment and prediction outcomes in many medical situations [ 64 ]. It allows medical professionals to embrace the proactive management of disease onset. Additionally, predictions are possible for identifying risk factors and drivers for each patient to help target healthcare interventions for better outcomes [ 3 ]. AI techniques can also help design and develop new drugs, monitor patients and personalise patient treatment plans [ 78 ]. Doctors benefit from having more time and concise data to make better patient decisions. Automatic learning through AI could disrupt medicine, allowing prediction models to be created for drugs and exams that monitor patients over their whole lives [ 79 ].

Clinical decision-making

One of the keyword analysis main topics is that AI applications could support doctors and medical researchers in the clinical decision-making process. According to Jiang et al. [ 64 ], AI can help physicians make better clinical decisions or even replace human judgement in healthcare-specific functional areas. According to Bennett and Hauser [ 80 ], algorithms can benefit clinical decisions by accelerating the process and the amount of care provided, positively impacting the cost of health services. Therefore, AI technologies can support medical professionals in their activities and simplify their jobs [ 4 ]. Finally, as Redondo and Sandoval [ 81 ] find, algorithmic platforms can provide virtual assistance to help doctors understand the semantics of language and learning to solve business process queries as a human being would.

Patient data and diagnostics

Another challenging topic related to AI applications is patient data and diagnostics. AI techniques can help medical researchers deal with the vast amount of data from patients (i.e., medical big data ). AI systems can manage data generated from clinical activities, such as screening, diagnosis, and treatment assignment. In this way, health personnel can learn similar subjects and associations between subject features and outcomes of interest [ 64 ].

These technologies can analyse raw data and provide helpful insights that can be used in patient treatments. They can help doctors in the diagnostic process; for example, to realise a high-speed body scan, it will be simpler to have an overall patient condition image. Then, AI technology can recreate a 3D mapping solution of a patient’s body.

In terms of data, interesting research perspectives are emerging. For instance, we observed the emergence of a stream of research on patient data management and protection related to AI applications [ 82 ].

For diagnostics, AI techniques can make a difference in rehabilitation therapy and surgery. Numerous robots have been designed to support and manage such tasks. Rehabilitation robots physically support and guide, for example, a patient’s limb during motor therapy [ 83 ]. For surgery, AI has a vast opportunity to transform surgical robotics through devices that can perform semi-automated surgical tasks with increasing efficiency. The final aim of this technology is to automate procedures to negate human error while maintaining a high level of accuracy and precision [ 84 ]. Finally, the -19 period has led to increased remote patient diagnostics through telemedicine that enables remote observation of patients and provides physicians and nurses with support tools [ 66 , 85 , 86 ].

This study aims to provide a bibliometric analysis of publications on AI in healthcare, focusing on accounting, business and management, decision sciences and health profession studies. Using the SLR method of Massaro et al. [ 11 ], we provide a reliable and replicable research protocol for future studies in this field. Additionally, we investigate the trend of scientific publications on the subject, unexplored information, future directions, and implications using the science mapping workflow. Our analysis provides interesting insights.

In terms of bibliometric variables, the four leading journals, Journal of Medical Systems , Studies in Health Technology and Informatics , IEEE Journal of Biomedical and Health Informatics , and Decision Support Systems , are optimal locations for the publication of scientific articles on this topic. These journals deal mainly with healthcare, medical information systems, and applications such as cloud computing, machine learning, and AI. Additionally, in terms of h-index, Bushko R.G. and Liu C. are the most productive and impactful authors in this research stream. Burke et al.’s [ 67 ] contribution is the most cited with an analysis of nurse rostering using new technologies such as AI. Finally, in terms of keywords, co-occurrence reveals some interesting insights. For instance, researchers have found that AI has a role in diagnostic accuracy and helps in the analysis of health data by comparing thousands of medical records, experiencing automatic learning with clinical alerts, efficient management of health services and places of care, and the possibility of reconstructing patient history using these data.

Second, this paper finds five cluster analyses in healthcare applications: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making. These technologies can also contribute to optimising logistics processes in health services and allowing a better allocation of resources.

Third, the authors analysing the research findings and the issues under discussion strongly support AI's role in decision support. These applications, however, are demonstrated by creating a direct link to data quality management and the technology awareness of health personnel [ 87 ].

The importance of data quality for the decision-making process

Several authors have analysed AI in the healthcare research stream, but in this case, the authors focus on other literature that includes business and decision-making processes. In this regard, the analysis of the search flow reveals a double view of the literature. On the one hand, some contributions belong to the positivist literature and embrace future applications and implications of technology for health service management, data analysis and diagnostics [ 6 , 80 , 88 ]. On the other hand, some investigations also aim to understand the darker sides of technology and its impact. For example, as Carter [ 89 ] states, the impact of AI is multi-sectoral; its development, however, calls for action to protect personal data. Similarly, Davenport and Kalakota [ 77 ] focus on the ethical implications of using AI in healthcare. According to the authors, intelligent machines raise issues of accountability, transparency, and permission, especially in automated communication with patients. Our analysis does not indicate a marked strand of the literature; therefore, we argue that the discussion of elements such as the transparency of technology for patients is essential for the development of AI applications.

A large part of our results shows that, at the application level, AI can be used to improve medical support for patients (Fig. 11 ) [ 64 , 82 ]. However, we believe that, as indicated by Kalis et al. [ 90 ] on the pages of Harvard Business Review, the management of costly back-office problems should also be addressed.

The potential of algorithms includes data analysis. There is an immense quantity of data accessible now, which carries the possibility of providing information about a wide variety of medical and healthcare activities [ 91 ]. With the advent of modern computational methods, computer learning and AI techniques, there are numerous possibilities [ 79 , 83 , 84 ]. For example, AI makes it easier to turn data into concrete and actionable observations to improve decision-making, deliver high-quality patient treatment, adapt to real-time emergencies, and save more lives on the clinical front. In addition, AI makes it easier to leverage capital to develop systems and facilities and reduce expenses at the organisational level [ 78 ]. Studying contributions to the topic, we noticed that data accuracy was included in the debate, indicating that a high standard of data will benefit decision-making practitioners [ 38 , 77 ]. AI techniques are an essential instrument for studying data and the extraction of medical insight, and they may assist medical researchers in their practices. Using computational tools, healthcare stakeholders may leverage the power of data not only to evaluate past data ( descriptive analytics ) but also to forecast potential outcomes ( predictive analytics ) and to define the best actions for the present scenario ( prescriptive analytics ) [ 78 ]. The current abundance of evidence makes it easier to provide a broad view of patient health; doctors should have access to the correct details at the right time and location to provide the proper treatment [ 92 ].

Will medical technology de-skill doctors?

Further reflection concerns the skills of doctors. Studies have shown that healthcare personnel are progressively being exposed to technology for different purposes, such as collecting patient records or diagnosis [ 71 ]. This is demonstrated by the keywords (Fig. 6 ) that focus on technology and the role of decision-making with new innovative tools. In addition, the discussion expands with Lu [ 93 ], which indicates that the excessive use of technology could hinder doctors’ skills and clinical procedures' expansion. Among the main issues arising from the literature is the possible de-skilling of healthcare staff due to reduced autonomy in decision-making concerning patients [ 94 ]. Therefore, the challenges and discussion we uncovered in Fig. 11 are expanded by also considering the ethical implications of technology and the role of skills.

Implications

Our analysis also has multiple theoretical and practical implications.

In terms of theoretical contribution, this paper extends the previous results of Connelly et al., dos Santos et al, Hao et al., Huang et al., Liao et al. and Tran et al. [ 2 , 19 , 20 , 21 , 22 , 24 ] in considering AI in terms of clinical decision-making and data management quality.

In terms of practical implications, this paper aims to create a fruitful discussion with healthcare professionals and administrative staff on how AI can be at their service to increase work quality. Furthermore, this investigation offers a broad comprehension of bibliometric variables of AI techniques in healthcare. It can contribute to advancing scientific research in this field.

Limitations

Like any other, our study has some limitations that could be addressed by more in-depth future studies. For example, using only one research database, such as Scopus, could be limiting. Further analysis could also investigate the PubMed, IEEE, and Web of Science databases individually and holistically, especially the health parts. Then, the use of search terms such as "Artificial Intelligence" OR "AI" AND "Healthcare" could be too general and exclude interesting studies. Moreover, although we analysed 288 peer-reviewed scientific papers, because the new research topic is new, the analysis of conference papers could return interesting results for future researchers. Additionally, as this is a young research area, the analysis will be subject to recurrent obsolescence as multiple new research investigations are published. Finally, although bibliometric analysis has limited the subjectivity of the analysis [ 15 ], the verification of recurring themes could lead to different results by indicating areas of significant interest not listed here.

Future research avenues

Concerning future research perspectives, researchers believe that an analysis of the overall amount that a healthcare organisation should pay for AI technologies could be helpful. If these technologies are essential for health services management and patient treatment, governments should invest and contribute to healthcare organisations' modernisation. New investment funds could be made available in the healthcare world, as in the European case with the Next Generation EU programme or national investment programmes [ 95 ]. Additionally, this should happen especially in the poorest countries around the world, where there is a lack of infrastructure and services related to health and medicine [ 96 ]. On the other hand, it might be interesting to evaluate additional profits generated by healthcare organisations with AI technologies compared to those that do not use such technologies.

Further analysis could also identify why some parts of the world have not conducted studies in this area. It would be helpful to carry out a comparative analysis between countries active in this research field and countries that are not currently involved. It would make it possible to identify variables affecting AI technologies' presence or absence in healthcare organisations. The results of collaboration between countries also present future researchers with the challenge of greater exchanges between researchers and professionals. Therefore, further research could investigate the difference in vision between professionals and academics.

In the accounting, business, and management research area, there is currently a lack of quantitative analysis of the costs and profits generated by healthcare organisations that use AI technologies. Therefore, research in this direction could further increase our understanding of the topic and the number of healthcare organisations that can access technologies based on AI. Finally, as suggested in the discussion section, more interdisciplinary studies are needed to strengthen AI links with data quality management and AI and ethics considerations in healthcare.

In pursuing the philosophy of Massaro et al.’s [ 11 ] methodological article, we have climbed on the shoulders of giants, hoping to provide a bird's-eye view of the AI literature in healthcare. We performed this study with a bibliometric analysis aimed at discovering authors, countries of publication and collaboration, and keywords and themes. We found a fast-growing, multi-disciplinary stream of research that is attracting an increasing number of authors.

The research, therefore, adopts a quantitative approach to the analysis of bibliometric variables and a qualitative approach to the study of recurring keywords, which has allowed us to demonstrate strands of literature that are not purely positive. There are currently some limitations that will affect future research potential, especially in ethics, data governance and the competencies of the health workforce.

Availability of data and materials

All the data are retrieved from public scientific platforms.

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Secinaro, S., Calandra, D., Secinaro, A. et al. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 21 , 125 (2021). https://doi.org/10.1186/s12911-021-01488-9

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Introduction, developmental research.

NORMATIVE STUDIES
QUALITATIVE RESEARCH
DESCRIPTIVE SURVEYS
CASE STUDIES
Full Chapter
Supplementary Content

Descriptive research is designed to document the factors that describe characteristics, behaviors and conditions of individuals and groups. For example, researchers have used this approach to describe a sample of individuals with spinal cord injuries with respect to gender, age, and cause and severity of injury to see whether these properties were similar to those described in the past. 1 Descriptive studies have documented the biomechanical parameters of wheelchair propulsion, 2 and the clinical characteristics of stroke. 3 As our diagram of the continuum of research shows, descriptive and exploratory elements are commonly combined, depending on how the investigator conceptualizes the research question.

Descriptive studies document the nature of existing phenomena and describe how variables change over time. They will generally be structured around a set of guiding questions or research objectives to generate data or characterize a situation of interest. Often this information can be used as a basis for formulation of research hypotheses that can be tested using exploratory or experimental techniques. The descriptive data supply the foundation for classifying individuals, for identifying relevant variables, and for asking new research questions.

Descriptive studies may involve prospective or retrospective data collection, and may be designed using longitudinal or cross-sectional methods (see Chapter 13 ). Surveys and secondary analysis of clinical databases are often used as sources of data for descriptive analysis. Several types of research can be categorized as descriptive, including developmental research, normative research, qualitative research and case studies. The purpose of this chapter is to describe these approaches.

Concepts of human development, whether they are related to cognition, perceptual-motor control, communication, physiological change, or psychological processes, are important elements of a clinical knowledge base. Valid interpretation of clinical outcomes depends on our ability to develop a clear picture of those we treat, their characteristics and performance expectations under different conditions. Developmental research involves the description of developmental change and the sequencing of behaviors in people over time. Developmental studies have contributed to the theoretical foundations of clinical practice in many ways. For example, the classic descriptive studies of Gesell and Amatruda 4 and McGraw 5 provide the basis for much of the research on sequencing of motor development in infants and children. Erikson's studies of life span development have contributed to an understanding of psychological growth through old age. 6

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500+ Quantitative Research Titles and Topics

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Quantitative research involves collecting and analyzing numerical data to identify patterns, trends, and relationships among variables. This method is widely used in social sciences, psychology , economics , and other fields where researchers aim to understand human behavior and phenomena through statistical analysis. If you are looking for a quantitative research topic, there are numerous areas to explore, from analyzing data on a specific population to studying the effects of a particular intervention or treatment. In this post, we will provide some ideas for quantitative research topics that may inspire you and help you narrow down your interests.

Quantitative Research Titles

Quantitative Research Titles are as follows:

Business and Economics

“Statistical Analysis of Supply Chain Disruptions on Retail Sales”
“Quantitative Examination of Consumer Loyalty Programs in the Fast Food Industry”
“Predicting Stock Market Trends Using Machine Learning Algorithms”
“Influence of Workplace Environment on Employee Productivity: A Quantitative Study”
“Impact of Economic Policies on Small Businesses: A Regression Analysis”
“Customer Satisfaction and Profit Margins: A Quantitative Correlation Study”
“Analyzing the Role of Marketing in Brand Recognition: A Statistical Overview”
“Quantitative Effects of Corporate Social Responsibility on Consumer Trust”
“Price Elasticity of Demand for Luxury Goods: A Case Study”
“The Relationship Between Fiscal Policy and Inflation Rates: A Time-Series Analysis”
“Factors Influencing E-commerce Conversion Rates: A Quantitative Exploration”
“Examining the Correlation Between Interest Rates and Consumer Spending”
“Standardized Testing and Academic Performance: A Quantitative Evaluation”
“Teaching Strategies and Student Learning Outcomes in Secondary Schools: A Quantitative Study”
“The Relationship Between Extracurricular Activities and Academic Success”
“Influence of Parental Involvement on Children’s Educational Achievements”
“Digital Literacy in Primary Schools: A Quantitative Assessment”
“Learning Outcomes in Blended vs. Traditional Classrooms: A Comparative Analysis”
“Correlation Between Teacher Experience and Student Success Rates”
“Analyzing the Impact of Classroom Technology on Reading Comprehension”
“Gender Differences in STEM Fields: A Quantitative Analysis of Enrollment Data”
“The Relationship Between Homework Load and Academic Burnout”
“Assessment of Special Education Programs in Public Schools”
“Role of Peer Tutoring in Improving Academic Performance: A Quantitative Study”

Medicine and Health Sciences

“The Impact of Sleep Duration on Cardiovascular Health: A Cross-sectional Study”
“Analyzing the Efficacy of Various Antidepressants: A Meta-Analysis”
“Patient Satisfaction in Telehealth Services: A Quantitative Assessment”
“Dietary Habits and Incidence of Heart Disease: A Quantitative Review”
“Correlations Between Stress Levels and Immune System Functioning”
“Smoking and Lung Function: A Quantitative Analysis”
“Influence of Physical Activity on Mental Health in Older Adults”
“Antibiotic Resistance Patterns in Community Hospitals: A Quantitative Study”
“The Efficacy of Vaccination Programs in Controlling Disease Spread: A Time-Series Analysis”
“Role of Social Determinants in Health Outcomes: A Quantitative Exploration”
“Impact of Hospital Design on Patient Recovery Rates”
“Quantitative Analysis of Dietary Choices and Obesity Rates in Children”

Social Sciences

“Examining Social Inequality through Wage Distribution: A Quantitative Study”
“Impact of Parental Divorce on Child Development: A Longitudinal Study”
“Social Media and its Effect on Political Polarization: A Quantitative Analysis”
“The Relationship Between Religion and Social Attitudes: A Statistical Overview”
“Influence of Socioeconomic Status on Educational Achievement”
“Quantifying the Effects of Community Programs on Crime Reduction”
“Public Opinion and Immigration Policies: A Quantitative Exploration”
“Analyzing the Gender Representation in Political Offices: A Quantitative Study”
“Impact of Mass Media on Public Opinion: A Regression Analysis”
“Influence of Urban Design on Social Interactions in Communities”
“The Role of Social Support in Mental Health Outcomes: A Quantitative Analysis”
“Examining the Relationship Between Substance Abuse and Employment Status”

Engineering and Technology

“Performance Evaluation of Different Machine Learning Algorithms in Autonomous Vehicles”
“Material Science: A Quantitative Analysis of Stress-Strain Properties in Various Alloys”
“Impacts of Data Center Cooling Solutions on Energy Consumption”
“Analyzing the Reliability of Renewable Energy Sources in Grid Management”
“Optimization of 5G Network Performance: A Quantitative Assessment”
“Quantifying the Effects of Aerodynamics on Fuel Efficiency in Commercial Airplanes”
“The Relationship Between Software Complexity and Bug Frequency”
“Machine Learning in Predictive Maintenance: A Quantitative Analysis”
“Wearable Technologies and their Impact on Healthcare Monitoring”
“Quantitative Assessment of Cybersecurity Measures in Financial Institutions”
“Analysis of Noise Pollution from Urban Transportation Systems”
“The Influence of Architectural Design on Energy Efficiency in Buildings”

Quantitative Research Topics

Quantitative Research Topics are as follows:

The effects of social media on self-esteem among teenagers.
A comparative study of academic achievement among students of single-sex and co-educational schools.
The impact of gender on leadership styles in the workplace.
The correlation between parental involvement and academic performance of students.
The effect of mindfulness meditation on stress levels in college students.
The relationship between employee motivation and job satisfaction.
The effectiveness of online learning compared to traditional classroom learning.
The correlation between sleep duration and academic performance among college students.
The impact of exercise on mental health among adults.
The relationship between social support and psychological well-being among cancer patients.
The effect of caffeine consumption on sleep quality.
A comparative study of the effectiveness of cognitive-behavioral therapy and pharmacotherapy in treating depression.
The relationship between physical attractiveness and job opportunities.
The correlation between smartphone addiction and academic performance among high school students.
The impact of music on memory recall among adults.
The effectiveness of parental control software in limiting children’s online activity.
The relationship between social media use and body image dissatisfaction among young adults.
The correlation between academic achievement and parental involvement among minority students.
The impact of early childhood education on academic performance in later years.
The effectiveness of employee training and development programs in improving organizational performance.
The relationship between socioeconomic status and access to healthcare services.
The correlation between social support and academic achievement among college students.
The impact of technology on communication skills among children.
The effectiveness of mindfulness-based stress reduction programs in reducing symptoms of anxiety and depression.
The relationship between employee turnover and organizational culture.
The correlation between job satisfaction and employee engagement.
The impact of video game violence on aggressive behavior among children.
The effectiveness of nutritional education in promoting healthy eating habits among adolescents.
The relationship between bullying and academic performance among middle school students.
The correlation between teacher expectations and student achievement.
The impact of gender stereotypes on career choices among high school students.
The effectiveness of anger management programs in reducing violent behavior.
The relationship between social support and recovery from substance abuse.
The correlation between parent-child communication and adolescent drug use.
The impact of technology on family relationships.
The effectiveness of smoking cessation programs in promoting long-term abstinence.
The relationship between personality traits and academic achievement.
The correlation between stress and job performance among healthcare professionals.
The impact of online privacy concerns on social media use.
The effectiveness of cognitive-behavioral therapy in treating anxiety disorders.
The relationship between teacher feedback and student motivation.
The correlation between physical activity and academic performance among elementary school students.
The impact of parental divorce on academic achievement among children.
The effectiveness of diversity training in improving workplace relationships.
The relationship between childhood trauma and adult mental health.
The correlation between parental involvement and substance abuse among adolescents.
The impact of social media use on romantic relationships among young adults.
The effectiveness of assertiveness training in improving communication skills.
The relationship between parental expectations and academic achievement among high school students.
The correlation between sleep quality and mood among adults.
The impact of video game addiction on academic performance among college students.
The effectiveness of group therapy in treating eating disorders.
The relationship between job stress and job performance among teachers.
The correlation between mindfulness and emotional regulation.
The impact of social media use on self-esteem among college students.
The effectiveness of parent-teacher communication in promoting academic achievement among elementary school students.
The impact of renewable energy policies on carbon emissions
The relationship between employee motivation and job performance
The effectiveness of psychotherapy in treating eating disorders
The correlation between physical activity and cognitive function in older adults
The effect of childhood poverty on adult health outcomes
The impact of urbanization on biodiversity conservation
The relationship between work-life balance and employee job satisfaction
The effectiveness of eye movement desensitization and reprocessing (EMDR) in treating trauma
The correlation between parenting styles and child behavior
The effect of social media on political polarization
The impact of foreign aid on economic development
The relationship between workplace diversity and organizational performance
The effectiveness of dialectical behavior therapy in treating borderline personality disorder
The correlation between childhood abuse and adult mental health outcomes
The effect of sleep deprivation on cognitive function
The impact of trade policies on international trade and economic growth
The relationship between employee engagement and organizational commitment
The effectiveness of cognitive therapy in treating postpartum depression
The correlation between family meals and child obesity rates
The effect of parental involvement in sports on child athletic performance
The impact of social entrepreneurship on sustainable development
The relationship between emotional labor and job burnout
The effectiveness of art therapy in treating dementia
The correlation between social media use and academic procrastination
The effect of poverty on childhood educational attainment
The impact of urban green spaces on mental health
The relationship between job insecurity and employee well-being
The effectiveness of virtual reality exposure therapy in treating anxiety disorders
The correlation between childhood trauma and substance abuse
The effect of screen time on children’s social skills
The impact of trade unions on employee job satisfaction
The relationship between cultural intelligence and cross-cultural communication
The effectiveness of acceptance and commitment therapy in treating chronic pain
The correlation between childhood obesity and adult health outcomes
The effect of gender diversity on corporate performance
The impact of environmental regulations on industry competitiveness.
The impact of renewable energy policies on greenhouse gas emissions
The relationship between workplace diversity and team performance
The effectiveness of group therapy in treating substance abuse
The correlation between parental involvement and social skills in early childhood
The effect of technology use on sleep patterns
The impact of government regulations on small business growth
The relationship between job satisfaction and employee turnover
The effectiveness of virtual reality therapy in treating anxiety disorders
The correlation between parental involvement and academic motivation in adolescents
The effect of social media on political engagement
The impact of urbanization on mental health
The relationship between corporate social responsibility and consumer trust
The correlation between early childhood education and social-emotional development
The effect of screen time on cognitive development in young children
The impact of trade policies on global economic growth
The relationship between workplace diversity and innovation
The effectiveness of family therapy in treating eating disorders
The correlation between parental involvement and college persistence
The effect of social media on body image and self-esteem
The impact of environmental regulations on business competitiveness
The relationship between job autonomy and job satisfaction
The effectiveness of virtual reality therapy in treating phobias
The correlation between parental involvement and academic achievement in college
The effect of social media on sleep quality
The impact of immigration policies on social integration
The relationship between workplace diversity and employee well-being
The effectiveness of psychodynamic therapy in treating personality disorders
The correlation between early childhood education and executive function skills
The effect of parental involvement on STEM education outcomes
The impact of trade policies on domestic employment rates
The relationship between job insecurity and mental health
The effectiveness of exposure therapy in treating PTSD
The correlation between parental involvement and social mobility
The effect of social media on intergroup relations
The impact of urbanization on air pollution and respiratory health.
The relationship between emotional intelligence and leadership effectiveness
The effectiveness of cognitive-behavioral therapy in treating depression
The correlation between early childhood education and language development
The effect of parental involvement on academic achievement in STEM fields
The impact of trade policies on income inequality
The relationship between workplace diversity and customer satisfaction
The effectiveness of mindfulness-based therapy in treating anxiety disorders
The correlation between parental involvement and civic engagement in adolescents
The effect of social media on mental health among teenagers
The impact of public transportation policies on traffic congestion
The relationship between job stress and job performance
The effectiveness of group therapy in treating depression
The correlation between early childhood education and cognitive development
The effect of parental involvement on academic motivation in college
The impact of environmental regulations on energy consumption
The relationship between workplace diversity and employee engagement
The effectiveness of art therapy in treating PTSD
The correlation between parental involvement and academic success in vocational education
The effect of social media on academic achievement in college
The impact of tax policies on economic growth
The relationship between job flexibility and work-life balance
The effectiveness of acceptance and commitment therapy in treating anxiety disorders
The correlation between early childhood education and social competence
The effect of parental involvement on career readiness in high school
The impact of immigration policies on crime rates
The relationship between workplace diversity and employee retention
The effectiveness of play therapy in treating trauma
The correlation between parental involvement and academic success in online learning
The effect of social media on body dissatisfaction among women
The impact of urbanization on public health infrastructure
The relationship between job satisfaction and job performance
The effectiveness of eye movement desensitization and reprocessing therapy in treating PTSD
The correlation between early childhood education and social skills in adolescence
The effect of parental involvement on academic achievement in the arts
The impact of trade policies on foreign investment
The relationship between workplace diversity and decision-making
The effectiveness of exposure and response prevention therapy in treating OCD
The correlation between parental involvement and academic success in special education
The impact of zoning laws on affordable housing
The relationship between job design and employee motivation
The effectiveness of cognitive rehabilitation therapy in treating traumatic brain injury
The correlation between early childhood education and social-emotional learning
The effect of parental involvement on academic achievement in foreign language learning
The impact of trade policies on the environment
The relationship between workplace diversity and creativity
The effectiveness of emotion-focused therapy in treating relationship problems
The correlation between parental involvement and academic success in music education
The effect of social media on interpersonal communication skills
The impact of public health campaigns on health behaviors
The relationship between job resources and job stress
The effectiveness of equine therapy in treating substance abuse
The correlation between early childhood education and self-regulation
The effect of parental involvement on academic achievement in physical education
The impact of immigration policies on cultural assimilation
The relationship between workplace diversity and conflict resolution
The effectiveness of schema therapy in treating personality disorders
The correlation between parental involvement and academic success in career and technical education
The effect of social media on trust in government institutions
The impact of urbanization on public transportation systems
The relationship between job demands and job stress
The correlation between early childhood education and executive functioning
The effect of parental involvement on academic achievement in computer science
The effectiveness of cognitive processing therapy in treating PTSD
The correlation between parental involvement and academic success in homeschooling
The effect of social media on cyberbullying behavior
The impact of urbanization on air quality
The effectiveness of dance therapy in treating anxiety disorders
The correlation between early childhood education and math achievement
The effect of parental involvement on academic achievement in health education
The impact of global warming on agriculture
The effectiveness of narrative therapy in treating depression
The correlation between parental involvement and academic success in character education
The effect of social media on political participation
The impact of technology on job displacement
The relationship between job resources and job satisfaction
The effectiveness of art therapy in treating addiction
The correlation between early childhood education and reading comprehension
The effect of parental involvement on academic achievement in environmental education
The impact of income inequality on social mobility
The relationship between workplace diversity and organizational culture
The effectiveness of solution-focused brief therapy in treating anxiety disorders
The correlation between parental involvement and academic success in physical therapy education
The effect of social media on misinformation
The impact of green energy policies on economic growth
The relationship between job demands and employee well-being
The correlation between early childhood education and science achievement
The effect of parental involvement on academic achievement in religious education
The impact of gender diversity on corporate governance
The relationship between workplace diversity and ethical decision-making
The correlation between parental involvement and academic success in dental hygiene education
The effect of social media on self-esteem among adolescents
The impact of renewable energy policies on energy security
The effect of parental involvement on academic achievement in social studies
The impact of trade policies on job growth
The relationship between workplace diversity and leadership styles
The correlation between parental involvement and academic success in online vocational training
The effect of social media on self-esteem among men
The impact of urbanization on air pollution levels
The effectiveness of music therapy in treating depression
The correlation between early childhood education and math skills
The effect of parental involvement on academic achievement in language arts
The impact of immigration policies on labor market outcomes
The effectiveness of hypnotherapy in treating phobias
The effect of social media on political engagement among young adults
The impact of urbanization on access to green spaces
The relationship between job crafting and job satisfaction
The effectiveness of exposure therapy in treating specific phobias
The correlation between early childhood education and spatial reasoning
The effect of parental involvement on academic achievement in business education
The impact of trade policies on economic inequality
The effectiveness of narrative therapy in treating PTSD
The correlation between parental involvement and academic success in nursing education
The effect of social media on sleep quality among adolescents
The impact of urbanization on crime rates
The relationship between job insecurity and turnover intentions
The effectiveness of pet therapy in treating anxiety disorders
The correlation between early childhood education and STEM skills
The effect of parental involvement on academic achievement in culinary education
The impact of immigration policies on housing affordability
The relationship between workplace diversity and employee satisfaction
The effectiveness of mindfulness-based stress reduction in treating chronic pain
The correlation between parental involvement and academic success in art education
The effect of social media on academic procrastination among college students
The impact of urbanization on public safety services.

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Descriptive Research | Definition, Types, Methods & Examples

Published on May 15, 2019 by Shona McCombes . Revised on June 22, 2023.

Descriptive research aims to accurately and systematically describe a population, situation or phenomenon. It can answer what , where , when and how questions , but not why questions.

A descriptive research design can use a wide variety of research methods to investigate one or more variables . Unlike in experimental research , the researcher does not control or manipulate any of the variables, but only observes and measures them.

When to use a descriptive research design, descriptive research methods, other interesting articles.

Descriptive research is an appropriate choice when the research aim is to identify characteristics, frequencies, trends, and categories.

It is useful when not much is known yet about the topic or problem. Before you can research why something happens, you need to understand how, when and where it happens.

Descriptive research question examples

How has the Amsterdam housing market changed over the past 20 years?
Do customers of company X prefer product X or product Y?
What are the main genetic, behavioural and morphological differences between European wildcats and domestic cats?
What are the most popular online news sources among under-18s?
How prevalent is disease A in population B?

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Descriptive research is usually defined as a type of quantitative research , though qualitative research can also be used for descriptive purposes. The research design should be carefully developed to ensure that the results are valid and reliable .

Survey research allows you to gather large volumes of data that can be analyzed for frequencies, averages and patterns. Common uses of surveys include:

Describing the demographics of a country or region
Gauging public opinion on political and social topics
Evaluating satisfaction with a company’s products or an organization’s services

Observations

Observations allow you to gather data on behaviours and phenomena without having to rely on the honesty and accuracy of respondents. This method is often used by psychological, social and market researchers to understand how people act in real-life situations.

Observation of physical entities and phenomena is also an important part of research in the natural sciences. Before you can develop testable hypotheses , models or theories, it’s necessary to observe and systematically describe the subject under investigation.

Case studies

A case study can be used to describe the characteristics of a specific subject (such as a person, group, event or organization). Instead of gathering a large volume of data to identify patterns across time or location, case studies gather detailed data to identify the characteristics of a narrowly defined subject.

Rather than aiming to describe generalizable facts, case studies often focus on unusual or interesting cases that challenge assumptions, add complexity, or reveal something new about a research problem .

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

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Study Designs in Medicine

Scientific studies can be described as “ a planned and systematic effort based on evidence for the solution of any health problems using data with high degree of accuracy ” ( 1 ). The main aims are to quantify disease prevalence, and compare interventions, predictions, association assessments or etiology assessments ( 2 ). A scientific study requires good planning including research protocol, ethical approval, data collection, data analysis, interpretation of data analysis results and publication. This study can help authors understand study designs in medicine.

Scientific studies can be classified as “Basic Studies”, “Observational Studies”, “Experimental (Interventional) Studies”, “Economic Evaluations” and “Meta-Analysis – Systematic Review”, as shown in Figure 1 .

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Study designs

BASIC STUDIES

Basic studies investigate the cause-outcome relationships between a dependent variable and independent variables, such as animal experiment, genetic and cell studies. Also, method development studies investigate the development and improvement of biochemical (e.g., enzymes, markers or genes), imaging (e.g., magnetic resonance) and biometric methods (e.g., statistical methods) ( 3 ). Several checklists have been developed to guide authors in the preparing, conducting and reporting stages of their studies. The ARRIVE checklist supplies transparency and accuracy in the animal experiments ( 1 ).

OBSERVATIONAL STUDIES

Observational studies can be defined as non-interventional and non-experimental ( 3 ). They do not contain any experiment or intervention methods. Investigated factors aren’t controlled, repetition of events aren’t generally possible and randomisation facilities are limited in these studies. However, their results are largely consistent with real life ( 4 ). They can be classified as descriptive or analytical, as shown in the Figure 1 .

Descriptive studies

Health problems or events as regards a particular disease or condition are detected and identified in these studies. They seek answers to the following questions about health problems or events: “What is it?”, “Where is it seen?”, “When is it seen?” and “Who are observed?” Descriptive statistics (mean, rate, etc.), frequency distributions and population parameters are determined by this kind of research.

Descriptive observational studies include case-report , case series and cross-sectional studies (descriptive or prevalence) . Patient and disease characteristics related to some interesting and remarkable type defined in a patient are called a “ case report ”. When the number of patients is more than one, this is called a “ case series ”. These are the most simple research types and do not contain a control group. Case series are usually starting points of the examined hypothesis in the case-control, cross-sectional or cohort studies ( 5 ). The use of CARE statement in the publication of a case report supplies transparency and accuracy ( 6 ).

Cross-sectional studies (descriptive or prevalence) can be described as prevalence studies and generally examine the prevalence, epidemiology or survey of a disease or clinical outcome. They reflect the situation of a disease or clinical outcome at a particular moment in a particular population ( 5 ).

Analytical (inferential) studies

Cross-sectional study.

Analytical cross-sectional studies are conducted in a specific time period which does not contain follow-up and enquires: “What is happening in a specific time period?” ( Figure 2 ) They try to explain potential causal associations between causes (exposures) and outcome (disease or clinical outcome). As a cohort study, they compare disease prevalence between exposure groups, and as a case-control study, they compare exposure between disease and healthy groups ( 2 ). Generally, they do not have a follow-up period.( 5 ) Checklists guide the authors in preparing, conducting and reporting stages of research. The STROBE statement for cross-sectional studies is a useful guideline for this design ( 1 ).

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Cross-sectional study design

Case-control study

Case-Control Studies are conducted retrospectively and enquire: “What happened in the past?” ( Figure 3 ). The cases are subjects selected according to presence of disease or clinical outcome. However, the control subjects are selected without disease or clinical outcome. The case and control groups are compared in terms of the presence of certain factors. Case group should be matched to the control group except for investigated factors. These are matched case-control study ( 5 ). The STROBE statement for case-control studies guides authors ( 1 ).

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Case-control study design

Diagnostic Accuracy Studies investigate the effect of a diagnostic method (such as imaging, pathological) compared with a gold standard method ( 3 ). They are similar to case-control studies. The STARD statement helps authors in designing, conducting and reporting diagnostic accuracy studies ( 1 ).

Cohort study

Cohort is a special group of people who have been selected according to some defining characteristics and they have certain disease risk factors or health outcome. Cohort Studies , also called follow-up studies, are generally prospective and enquire: “What will happen in the future?” ( Figure 4 ) Individuals are followed over time in cohort studies, and researchers assess exposure and outcome during follow-up ( 2 ). Cohort studies investigate the effect of prognostic factors (such as age, presence of hypertension and cholesterol level) on a clinical outcome (such as disease) ( 3 ).

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Cohort study design

Moreover, cohort studies can be conducted retrospectively; these are called “Historical Cohort Study”. Cohort Studies produce the most reliable clinical evidence among the observational studies due to the fact that they identify clinical or health outcomes based on exposure ( 5 ). The STROBE statement for cohort studies helps authors ( 1 ).

EXPERIMENTAL (INTERVENTIONAL) STUDIES

Experimental or interventional studies compare the effect of treatments or interventions with control in humans. Placebo or different treatment(s) or intervention(s) may be used as control. Experimental studies have to be transparent and evidence-based. In these studies, randomisation methods can be used, investigated factors are controlled, cause-effect relationships are evidenced and an experiment can be repeated as much as desired. However, their results are always not appropriate for real life ( 4 ). They can be conducted in four phases ( 7 ).

Phase I study is conducted in a small number of healthy volunteers (e.g. 20–80) to determine whether a drug or treatment method is safe . Pharmacokinetic and pharmacodynamic measurements are done in these studies. Maximum safe dose, movement of the drug in the body and dose-response relationship are examined. Phase II study is conducted in a target population (75–300) to determine the treatment effect of a drug or treatment method. Standard treatment method has to be compared with placebo in Phase II clinical trials. Phase III study is conducted on many patients (e.g., 1000–2000) to determine whether the new drug is better than the standard drug. It is done in order to reveal that a drug is not only safe and effective, but also has better and less adverse effects than standard treatment . Usually, at least two RCTs are required in this phase.

Clinical trials (Phase IV) are called post-marketing product surveillance studies, which are conducted on patients in daily life; the new drug had been approved by the Ministry in this phase. They evaluate the adverse effect and various additional indications of a new drug ( 7 , 8 ).

Observational Drug Studies are other forms of Phase IV clinical trials. They collect the data about a spontaneously prescribed drug from the patients with diagnosed and ongoing treatment. In these studies, additional information from a larger population may be obtained in order to compare the results of experimental clinical drug trials ( 4 ).

Randomised controlled trial (RCT)

Randomised controlled trials produce the strongest evidence among clinical trials due to the fact that patients are allocated to treatments or interventions randomly (equal chance). In these studies, two or more clinical treatments or intervention are compared. RCTs are expensive and slow, however, their level of evidence is higher due to the fact that randomisation removes the allocation bias ( 2 ). Many respected journals endorse the CONSORT statement in order to improve the scientific quality and transparency of RCTs. Authors should be used to the CONSORT statement as a guideline in RCTs ( 1 ).

When the preference of participants is not to receive a placebo or control, randomisation procedure is not applied. These studies are called Non-Randomised Controlled Studies . They are inexpensive especially if they are conducted as retrospective and representative sample of patients in clinical practice. However, they are open to bias ( 2 ).

Self-controlled study

Self-Controlled Studies do not include an independent control group; they use the patients as their own controls. At least two measurements are obtained at different times from the same patients (e.g., preop, postop 1. month, and 6. month measurements) and the effect of treatment or intervention is determined ( 5 ).

Crossover study

Crossover Studies include both of self-control and independent groups. They are powerful, but not always possible to apply. In crossover studies, patients are assigned two groups (placebo or experimental treatment). After a time, the research is interrupted for a washout period (at least two weeks), and patients receive no treatments during this period. At the end of the washout period, the experimental treatment group receives the placebo and the placebo group receives to the experimental treatment ( 5 ). The effect of treatment or intervention is determined by comparisons of both self-control and independent groups in crossover design ( Figure 5 ).

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Crossover study design

Properties of experimental studies

Direction of studies.

Studies can be classified as prospective or retrospective according to direction. In prospective studies, a specific sample is followed over a certain period in order to determine outcome from the reasons. The research question is: “What will happen in the future?” Retrospective studies generally compare the outcome of diagnostic and treatment methods. Data are obtained from patient records. The research question is: “What happened in the past?” ( 5 ).

Randomisation

In randomisation method the subjects or patients who will be included in the study are assigned to treatment groups with equal probability (chance) in the beginning of study. A computerized software is widely used for allocating the subjects/patients to the groups in the randomisation processes. Studies can be classified as i) randomised or ii) non-randomised. Randomised Controlled Studies (RCTs) produce the most reliable results among all research types.

Blinding describes that one or more of the physicians, researchers, patients and data analysts do not know which treatment subjects have received. It ensures reliable and objective results preventing bias. Blinding can be defined as three different types (single, double and triple). Single-blind: either subjects or researchers know which treatment subjects have received. Double-blind: both subjects and researchers do not know which treatment subjects have received. Triple-blind: in addition to the subjects and researcher(s), statisticians/monitors do not know which treatment subjects have received ( 5 ).

Confounding and interaction

Confounding can be defined as disruption of the relationships between two variables due to the effect of third variable. A confounder variable is associated both with causal and outcome variables ( 9 ). Two or higher independent variables have different effect on outcome variable to independent effect of each. This situation can be defined as interaction .

ECONOMIC EVALUATIONS

Cost Analysis is an economic analysis method that estimates total cost of a particular disease or health condition on society. Direct and indirect costs attributed to a specific disease are included in this method. It is also called “cost of illness”. Cost-Minimisation Analysis compares two alternative drugs’ (or interventions) costs and outcomes in order to determine the least costly drug (or intervention). However, it is quite difficult to find two alternative drugs which are equally effective and safe. Thus, it is rarely used in economic evaluations. Cost-Utility Analysis is an economic evaluation method comparing two alternative drugs (or interventions) costs and outcomes in order to determine the most useful drug (or intervention). Outcomes in these studies are measured in preference or utility of patients, and, generally, quality-adjusted life year (QALY) or disability-adjusted life year (DALY) are used as an outcome. Cost-Effectiveness Analysis compares two alternative drugs (or interventions) costs and clinical outcomes in order to determine the most effective drug (or intervention). Outcomes are measured by clinical parameters. It is the most widely used economic evaluation method. Cost-Benefit Analysis is an economic evaluation method, in which cost and benefit of alternative interventions are expressed in monetary units. Thus, it is rarely used in economic evaluations ( 8 ).

META-ANALYSIS AND SYSTEMATIC REVIEW

Several clinical studies (RCTs or Cohort) may be conducted in a clinical area over a period of years in different parts of the world. The results may be different and there may be different properties such as sample size and multicentre. Meta-Analysis combines the statistical results of different studies in a particular clinical area ( 7 , 9 ). The PRISMA statement guides the authors in the preparation of Meta-Analysis ( 1 ).

Systematic Review evaluates and interprets the evidence of all studies conducted in a clinical area ( 9 ). The main difference from Meta-Analysis is that it combines the evidence of different studies based on interpretation instead of combining statistical results.

Evidence level of the medical studies

The evidence pyramid shows the evidence level of a scientific study in clinical practices. The evidence pyramid of scientific medical studies is shown in Figure 6 . According to the evidence pyramid, the “Meta-Analysis/Systematic Review” produces the most reliable evidence, while “ in vitro study” produces the lowest reliable evidence ( 10 ).

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Evidence pyramid for medical studies

In conclusion, authors should correctly report the study design in the method section of their studies. Also, if randomisation, stratification or blinding methods are used, they should be reported in this section. Generally, studies are conducted on a sample, so sample size should be a sufficient number and representative of population in structural terms. Thus, determination of sample size, selection method of sample, inclusion and exclusion criteria should be explained in detail in the method section. Use of the checklists ( CONSORT statement for RCTs, ARRIVE for animal experiments and STROBE statement for cross-sectional, case-control and cohort studies, CARE statement for case report and PRISMA statement for meta-analysis) may prevent bias and guide authors in the preparation of their studies.

Center for Security and Emerging Technology

Assessing the next frontier in medical research: artificial intelligence and computational biology.

Charliste Hampton

Artificial intelligence is becoming more integrated into the sciences. One of the scientific fields experiencing this is computational biology, which uses computer modeling to understand biological mechanisms and systems. This blog post provides an understanding of important research trends in these subject areas, and how advancements in AI can improve the speed and efficiency of computational biology to improve human health and well-being.

Charliste Hampton was an inaugural research intern at CSET during the summer of 2024 and is a senior at Spelman College in Atlanta, Georgia.

How Does Computational Biology Work and What Are the Benefits?

Computational biology uses computers to understand biological mechanisms and systems with stored data. It can allow scientists to recreate, model, and predict various biological systems, from large organ interactions to smaller systems like simple cell function. Computational biology has already facilitated major scientific discoveries, and its benefits are quite significant. For example, humans are composed of tens of thousands of genes, and for years, scientists could only base scientific innovations and new drugs on snippets of human DNA. The Human Genome Project used computational biology to overcome this challenge by mapping many DNA fragments into a full human genome for the first time, helping researchers to see the correlations between genes and diseases and use that knowledge to create therapies. Virtual screening is another computational biological technique that sorts through databases of drug molecules and virtually tests thousands of them to find compatible drug candidates. This approach limits the pool of compounds that a researcher needs to individually test in a laboratory. Techniques like these have already proven themselves to be valuable assets to the research community and invaluable tools for improving human health. These techniques aid scientific innovation and medicine by creating treatment plans for blood diseases , neurological disorders, and other illnesses.

Integration of AI in Computational Biology

Artificial intelligence has the potential to improve the speed and efficiency of computational biology, which could lead to benefits for scientific innovation, human health and medicine, as well as the economy. AI can assist scientists by sorting and identifying patterns in biological data, simulating existing biological mechanisms, and predicting or generating new testable biological structures. Major U.S. policy recognizes these benefits too, including the Biden administration’s 2022 Executive Order on Advancing Biotechnology and Biomanufacturing , which states that “biotechnology harnesses the power of biology to create new services and products, which provide opportunities to grow the United States economy and workforce and improve the quality of our lives and the environment.”

Why is AI Useful for Computational Biology?

AI can be a useful addition to computational biology and future research innovations. For example, computational biologists used AI to address pressing problems like antimicrobial resistance , which contributes to over 4.9 million deaths per year. AI models performed virtual screening through the Broad Institute’s Drug Repurposing Hub , a database with over 6,000 compounds, and found that the drug Halicin, created years ago for diabetes, has the capacity to kill resistant bacteria. Furthermore, generative AI systems like AlphaFold can model potential protein structures using existing databases of known protein structures. This benefits drug design because it enables scientists to explore what compounds will be most compatible when creating drug therapies and treatment plans. Exploring the benefits of AI can create and contribute to a more cost efficient and less time-consuming drug development process, saving lives and improving patient health.

Research Trends in AI + Computational Biology

Our research team used data from CSET’s Emerging Technology Observatory to evaluate the current state of AI’s use for computational biology. This area of research is growing, and will likely continue to do so in the coming years.

The Research Almanac is a user-friendly ETO tool that shows global trends in emerging technology research topics that are associated with AI. It identifies AI-related scholarly publications within a range of topic areas, and summarizes relevant metrics like the number of articles published per year, the institutions and countries that are publishing them, and associated patents. This blog post references the “AI + computational biology” page of the Research Almanac.

Computational Biology + AI Research is Growing

Figure 1: Global AI + Computational Biology Research Over Time

Source: ETO Research Almanac. For more, see: https://almanac.eto.tech/topics/ai-applications-computational-biology/ .

There are over 14,000 published AI + computational biology articles from 2017-2022.
While AI + computational biology articles only make up around 1% of all AI articles in the Research Almanac, the topic grew by 85%, suggesting that it may continue to grow globally and be an area of interest.
Other biology topics in the Research Almanac are also growing in terms of their publishing presence. AI + Neuroscience grew 170% from 2017-2022 while AI + Pharmacology grew 291% and AI + Genetics grew 167%. This suggests that artificial intelligence is a fast-growing tool that is beneficial to a diverse range of biological studies and research.

For further details on how the Research Almanac classifies articles, see the documentation .

AI + Computational Biology Research is Global

Figure 2: AI + Computational Biology Research Over Time

For AI + computational biology research publications between 2017-2021, 28% have authors from China, 21% have authors from the United States, and 20% have authors from European countries. The United Kingdom is the third leading country with 7% of published articles. This suggests that the United States and China can have a significant impact on the field as leading contributors to AI + computational biology publications.
There is a range of global interest in research with AI + computational biology which suggests that this may be an area that is beneficial to advancing scientific innovations or the quality of life of individuals.

Fast-Growing AI + Computational Biology Research is Varied

Our research team looked at a different ETO tool, the Map of Science , which collects and organizes the research literature, revealing key trends, hotspots, and concepts in global science and technology.

The Map of Science includes 76 AI + computational biology clusters, of which 15 have predicted extreme growth. Below are some of the particularly interesting fast-growing clusters.

AI and computational biology can help find more efficient cancer treatments. Computational biological databases are filled with multiple chemical compounds and molecules. AI models can sort through these molecules, create simulations, and analyze the data in these servers to be able to predict new combinations of drugs in order to combat constantly evolving tumor cells.
Databases can also be filled with data from microorganisms, and so the COVID-19 pandemic sent the computational biology research community into a race to find a vaccine. Researchers used the predictive power of AI to build biological models and simulations that predict which chemical compounds are best suited as a defense against viruses and other microorganisms like bacteria.
The human microbiome is filled with millions of different microorganisms, alongside numerous genes that contribute to our health and well-being. Computational biologists are using AI in order to sort and predict what types of drugs, treatment plans, and therapies would be most beneficial for each individual’s specific microbiome.

While the sector of AI + computational biology is a small percentage of AI research, its high growth rate suggests that it has the potential to be an advantageous biotechnology.

U.S. policymakers should be aware that advances in AI are not just valuable to the tech industry but also to scientific innovations and even medicine more broadly. Just like our research community, machine learning programs are already benefiting our healthcare system by assisting physicians in predicting more efficient healthcare treatments and improving the quality of patient care. Policymakers should consider the positive impacts of AI and computational biology, especially as it continues to make significant impacts in research and medicine improving overall quality of life.

About the Author

I am a senior and a biology major at Spelman College in Atlanta, Georgia. Growing up in the rural south, I witnessed health disparities and environmental injustices, and these experiences inspired my passion for advocacy and research. As an intern for Georgetown University’s Center for Security and Emerging Technology in Washington, D.C., I have been able to explore artificial intelligence and its potential to benefit biotechnology and medical discoveries. Being from a community with limited access to healthcare served as an important personal and eye-opening experience for me. I believe that investing in technology will likely have a positive impact on communities like mine. Precision medicine is just one of the areas of particular interest to me, as it approaches personalized treatment plans that do not just take into account genetic differences but the environmental and demographic differences of each individual. Advocacy is an important aspect of my life and it fuels my passion for scientific innovation, health equity, and research as a means to improve the quality of life for so many people.

Top-cited research = the 10% of articles in each year with the most citations. Note that some articles lack information about author nationality, and articles without English-language titles or abstracts are omitted. For further details, see the Research Almanac documentation .

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The Antimicrobial Resistance Research Landscape and Emerging Solutions

Antimicrobial resistance (AMR) is one of the world’s most pressing global health threats. Basic research is the first step towards identifying solutions. This brief examines the AMR research landscape since 2000, finding that the amount… Read More

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Study Designs in Medicine. Scientific studies can be described as " a planned and systematic effort based on evidence for the solution of any health problems using data with high degree of accuracy " (1). The main aims are to quantify disease prevalence, and compare interventions, predictions, association assessments or etiology assessments ...
Assessing the Next Frontier in Medical Research: Artificial
The Research Almanac is a user-friendly ETO tool that shows global trends in emerging technology research topics that are associated with AI. It identifies AI-related scholarly publications within a range of topic areas, and summarizes relevant metrics like the number of articles published per year, the institutions and countries that are ...
Any recommendation for a research proposal related to Medical
This would be a great time focusing your study in pandemic. Here are my suggestions: - Level of Knowledge of Medical Technology Interns on COVID-19 Laboratory Safety and Sample Processing ...