Biomedical (17) | [ - ] |
| Medical licensing examination (12) | [ - ] |
| Clinical (general) (15) | [ - ] |
| Psychiatry (10) | [ , - ] |
| Oncology (5) | [ - ] |
| Cardiology (4) | [ - ] |
| Ophthalmology (3) | [ - ] |
| Neurology (3) | [ , , ] |
| Orthopedics (2) | [ , ] |
| Clinical trials (2) | [ , ] |
| Intensive care (2) | [ , ] |
| Geriatrics (2) | [ , ] |
| Radiology (2) | [ , ] |
| Nuclear medicine (1) | [ ] |
| Hepatology (1) | [ ] |
| Endocrinology (1) | [ ] |
| Plastic surgery (1) | [ ] |
| Gastroenterology (1) | [ ] |
| Genetics (1) | [ ] |
| Nursing (1) | [ ] |
|
| Biomedical (13) | [ - ] |
| Clinical (general) (15) | [ , , - ] |
| Psychiatry (1) | [ ] |
|
| Biomedical (9) | [ , , , , , , , , ] |
| Clinical (general) (6) | [ , , , - ] |
| Oncology (2) | [ , ] |
| Psychiatry (1) | [ ] |
| Medical insurance (1) | [ ] |
Terminology Use
In our review, the consistency of terminology use around prompt engineering was investigated, particularly concerning its 3 paradigms: PD, PL, and PT. Across the papers, we meticulously tracked instances where the terminology was applied differently to the definitions used in the literature and described in the introduction. Notably, PL was used to refer to PD 4 times [ 12 , 13 , 67 , 86 ] and PT once [ 119 ], while PT was used 5 times to describe PL [ 88 , 96 , 97 , 99 , 114 ] and twice for PD [ 23 , 43 ]. Terminology inconsistencies were identified in only 12 studies. Consequently, while there remains some degree of inconsistency, a significant majority of 102 papers adhered to the definitions identified as commonly used terminology.
Language of Study
Considering the latest developments in NLP research encompassing languages beyond English [ 124 ], reporting the language of study is crucial. Several papers do not explicitly state the language of study. In some cases, the language can be inferred from prompt illustrations or examples. In the least informative cases, only the data set of the study is disclosed, indirectly hinting at the language.
Table 2 illustrates the language distribution among the selected papers, noting whether languages are explicitly mentioned, implicitly inferred from prompt illustrations, or simply not stated but implied from the used data set. The language used in 2 papers [ 60 , 68 ] remains unknown.
Language and type of venue | Stated , n (%) | Inferred , n (%) | Not stated , n (%) | Total, n (%) |
|
| All | 37 (32.5) | 48 (42.1) | 11 (9.6) | 96 (84.2) |
| Medical informatics | 16 (14) | 9 (7.9) | 2 (1.8) | 27 (23.7) |
| Computer science | 8 (7) | 18 (15.8) | 1 (0.9) | 27 (23.7) |
| Preprint | 9 (7.9) | 12 (10.5) | 5 (4.4) | 26 (22.8) |
| Clinical | 1 (0.9) | 8 (7) | 3 (2.6) | 12 (10.5) |
| Other | 3 (2.6) | 1 (0.9) | 0 (0) | 4 (3.5) |
|
| All | 18 (15.8) | 0 (0) | 0 (0) | 18 (15.8) |
|
| All | 3 (2.6) | 0 (0) | 0 (0) | 3 (2.6) |
|
| All | 3 (2.6) | 0 (0) | 0 (0) | 3 (2.6) |
|
| All | 2 (1.8) | 0 (0) | 0 (0) | 2 (1.8) |
|
| All | 2 (1.8) | 0 (0) | 0 (0) | 2 (1.8) |
|
| All | 2 (1.8) | 0 (0) | 0 (0) | 2 (1.8) |
|
| All | 2 (1.8) | 0 (0) | 0 (0) | 2 (1.8) |
|
| All | 0 (0) | 0 (0) | 1 (0.9) | 1 (0.9) |
|
| All | 1 (0.9) | 0 (0) | 0 (0) | 1 (0.9) |
|
| All | 1 (0.9) | 0 (0) | 0 (0) | 1 (0.9) |
|
| All | 1 (0.9) | 0 (0) | 0 (0) | 1 (0.9) |
|
| All | 1 (0.9) | 0 (0) | 0 (0) | 1 (0.9) |
|
| All | 1 (0.9) | 0 (0) | 0 (0) | 1 (0.9) |
|
| All | 0 (0) | 0 (0) | 2 (1.8) | 2 (1.8) |
a Stated in the paper.
b Inferred from prompt figures and examples.
c Inferred from the data set.
Notably, English dominates with 84.2% (n=96) of the selected papers, followed by Chinese at 15.7% (n=18). Then, the other languages are relatively rare, often appearing in studies featuring multiple languages. It is worth mentioning that languages besides English are usually explicitly stated, with the exception of a paper studying Korean [ 63 ]. In total, the language had to be inferred from prompt figures and examples in 48 papers, all in English.
Choice of LLMs
Given the diverse array of LLMs available, spanning general or medical, open-source or proprietary, and monolingual or multilingual models, alongside various architectural configurations (encoder, decoder, or both), our study investigates LLM selection across prompt paradigms.
Figure 3 outlines prevalent LLMs categorized by prompt paradigms, though it is not exhaustive and only includes commonly encountered architectures. For example, while encoder-decoder models are absent in PT in Figure 3 , there are a few instances where they are used [ 95 , 110 ].
ChatGPT’s popularity in PD is unsurprising, given its accessibility. Models from Google, PaLM, and Bard (subsequently rebranded Gemini), all falling under closed models, are also prominent. Among open-source instruct-based LLMs, fewer are used, notably those based on LLaMA-2 with 7 occurrences.
In PL, encoder models, those following the BERT architecture, dominate, covering both general and specialized variants. There are occasional uses of decoder models like GPT-2 in PL-based tasks [ 103 , 105 ]. PT involves all model types, with a preference toward encoders. Further details on the models used are available in Multimedia Appendix 3 .
Topic Domain and NLP Task Trends
Figure 4 [ 16 , 20 , 26 , 41 , 47 , 88 - 123 ] illustrates the target tasks used in the PL and PT papers. PL-focused papers predominantly address classification-based tasks such as text classification, named entity recognition, and relation extraction, with text classification being particularly prominent. This aligns with the nature of PL, which centers around an MLM objective. Among other tasks, a study based on text generation [ 111 ] makes use of PL to predict masked tokens from partial patient records, aiming to generate synthetic electronic health records. Conversely, PT papers tend to exhibit a slightly broader range of tasks.
Figure 5 [ 10 - 87 ] presents the same analysis for PD-based papers. Unlike PL and PT, a prominent trend observed is that several studies focus on real-world board examinations. Notably, these studies predominantly center around tasks involving answering multiple-choice questions (MCQs). It is worth noting that although MCQs might be cast as a classification task, in practice, it is cast as a generation task using causal LLMs. It is interesting to note that none of the selected PD papers propose the task of entity linking, despite the clear opportunity of leveraging LLMs’ in-context learning ability for medical entity linking.
Prompt Engineering Techniques
We extensively investigated the used prompt techniques: among PD papers, 49 studies used zero-shot prompting, 23 used few-shot prompting, and 10 used one-shot prompting. Few shot tends to outperform in MCQs, but its advantage over zero shot is inconsistent in other NLP tasks. We propose a comprehensive summary of the existing techniques in Table 3 .
As shown in Table 3 , chain-of-thought (CoT) prompting [ 2 ] stands as the most common technique, followed by the persona pattern. In medical MCQs, various attempts with CoT can lead to different reasoning pathways and answers. Hence, to improve accuracy, 2 studies [ 19 , 20 ] used self-consistency, a method involving using multiple CoT prompts and selecting the most frequently occurring answer through voting.
Flipped interaction was used for simulation tasks, such as doctor-patient engagement [ 60 ] or to provide clinical training to medical students [ 81 ]. Emotion enhancement was applied in mental health contexts [ 58 , 60 ], allowing the LLM to produce emotional statements.
More innovative prompt engineering techniques include k-nearest neighbor few-shot prompting [ 19 ] and pseudoclassification prompting [ 78 ]. The former uses the k-nearest neighbor algorithm to select the k-closest examples in a large annotated data set based on the input before using them in the prompt, and the latter presents to the LLMs all possible labels, asking the model to respond with a binary output for each provided label. Despite its potential, tree-of-thoughts pattern use was limited, with only 1 instance found among the papers [ 77 ].
Prompt techniques | Description | Prompt template examples | Count papers | References |
Chain-of-thought (CoT) | Asking the large language model (LLM) to provide the reasoning before answering. | | 17 | [ , , , , , , , , , , , , , , , , ] |
Persona (role-defining) | Assigning the LLM a particular role to accomplish a task related to that role. | | 10 | [ , , , , - , , , ] |
Ensemble prompting | Using multiple independent prompts to answer the same question. The final output is decided by majority vote. | | 4 | [ , , , ] |
Scene-defining | Simulating a scene related to the addressed task. | | 3 | [ , , ] |
Prompt-chaining | Separating a task into multiple subtasks, each resolved with a prompt. | | 3 | [ , , ] |
Flipped interaction | Making the LLM take the lead (eg, asking questions) and the user interacting with it passively. | | 2 | [ , ] |
Emotion enhancement | Making the LLM more or less expressing human-like emotions. | | 2 | [ , ] |
Prompt refinement | Using the LLM to refine the prompt such as translating the prompt or rephrasing it. | | 2 | [ , ] |
Retrieval-augmented generation | Combining an information retrieval component with a generative LLM. Snippets extracted from documents are fed into the system along with the input prompt to generate an enriched output. | | 2 | [ , ] |
Self-consistency (CoT ensembling) | Ensemble prompting each prompt using CoT. Ideal if a problem has many possible reasoning paths. | | 2 | [ , ] |
Emerging Trends
Figure 6 illustrates a chronological polar pie chart of selected papers and their citation connections, identifying five highly cited papers: (1) Agrawal et al [ 40 ] demonstrate GPT-3’s clinical task performance, especially in named entity recognition and relation extraction through thorough PD. (2) Kung et al [ 36 ] evaluate ChatGPT’s (GPT-3.5) ability for the United States Medical Licensing Examination, shortly after the public release of ChatGPT. (3) Singhal et al [ 20 ] introduce MultiMedQA and HealthSearchQA benchmarks. The paper also presents instruction PT for domain alignment, a novel paradigm that entails learning a soft prompt prior to the LLM general instruction, which is usually written as a hard prompt. Using this approach on FlanPaLM led to the development of Med-PaLM, improving question answering over FlanPaLM. (4) Nori et al [ 27 ] evaluate GPT-4 on the United States Medical Licensing Examination and MultiMedQA, surpassing previous state-of-the-art results, including GPT-3.5 and Med-PaLM. (5) Luo et al [ 26 ] release BioGPT, a fine-tuned variant of GPT-2 for biomedical tasks, achieving state-of-the-art results on 6 biomedical NLP tasks with suffix-based PT.
Trends in PD
As shown in Figure 6 , the PD paradigm presents multiple trends: all papers disseminated in clinical-based venues, and 27 of 33 (82%) of the encountered preprints adhere to this paradigm. Furthermore, we observed a significant focus on work involving frozen LLMs within the PD domain. This trend is likely due to the frequent use of ChatGPT in 74 instances, as depicted in Figure 3 , despite OpenAI offering fine-tuning capabilities for the model. It is worth mentioning that 46 of 78 (59%) PD papers do not include any baseline, including human comparison. This gap will be further explored in a subsequent section.
Trends in PL and PT
Among PL and PT papers, computer science and medical informatics are the most prevalent venues. Although PL has drawn attention to the idea of adapting the MLM objective to downstream tasks without needing to further update the LLM weights, many studies still opt to fine-tune their LLMs, with a nonnegligible amount of them evaluating in few-shot settings [ 89 , 92 , 93 , 112 ]. Unlike PD, PL and PT usually include a baseline, with it often being a traditional fine-tuning version of the evaluated model [ 92 , 93 , 95 ] to compare it against novel prompt-based paradigms. These studies came to a common conclusion, being that PL is a promising alternative to traditional fine-tuning in few-shot scenarios.
There are 2 ways for conducting PL: one involves filling in the blanks within a text, known as cloze prompts, while the other consists in predicting masked tokens at the end of the sequence, referred to as prefix prompts. A distinct advantage of the latter approach is its compatibility with autoregressive models, as they exclusively predict the appended masks. Among the 29 PL papers, 21 (72%) of them propose cloze prompts, while 15 (52%) use prefix prompting. The involved NLP tasks are well-distributed across these 2 prompt patterns. Another crucial component of PL is the verbalizer. As PL revolves around predicting masked tokens, classification-based tasks require mapping manually selected relevant tokens to each class (manual verbalizer). Alternatively, some studies propose a soft verbalizer, akin to soft prompts, which automatically determines the most relevant token embedding for each label through training. Of the 29 PL papers selected, 16 (55%) studies explicitly mention the use of a manual verbalizer, while 2 explored both verbalizers to assess performance [ 101 , 110 ]. Only 1 exclusively used a soft verbalizer [ 89 ]. Another study does not use any verbalizer, as it focuses on generating synthetic data by filling the blanks [ 111 ]. Notably, 8 (28%) studies did not report any mention regarding the verbalizer methodology.
Hard prompts, which are related to PD and PL, involve manually crafted prompts. Regarding PT, optimal prompts are attainable through soft prompting (ie, prompts that are trained on a training data set), yet, determining the appropriate soft prompt length remains obscure. In total, 5 of 19 (26%) PT studies tried various soft prompt lengths and reported their corresponding performances [ 26 , 105 , 118 , 119 , 122 ]. While there is no definitive optimal prompt length, a trend emerges: optimal soft prompt length typically exceeds 10 tokens. Surprisingly, 8 (42%) papers omit reporting the soft prompt length. Regarding the placement of soft prompts in relation to the input and the mask, consensus is lacking. A total of 5 (26%) papers prepend the soft prompt at the input’s outset, while 4 (21%) append it as a suffix. One paper uses both strategies in a single prompt template [ 95 ]. Some innovative methods involve inserting a single soft prompt for each entity that needs to be identified in entity-linking tasks or using token-wise soft prompts, where each token in the textual input is accompanied by a distinct soft prompt. The position of soft prompts remains unreported in 5 (26%) studies. Finally, according to the 6 (32%) studies that used mixed prompts [ 90 , 91 , 95 , 101 , 105 , 110 ] (a combination of hard and soft prompts), it has consistently been reported that mixed prompts lead to a better performance than hard prompts alone.
Baseline Comparison
Only 62 of the screened papers reported comparisons to established baselines. These include traditional deep learning approaches (eg, fine-tuning approach), classical machine learning algorithms (eg, logistic regression), naive systems (eg, majority class), or human annotation. The remaining papers solely explored prompt-related solutions, without including baseline comparisons. Tables 4 - 6 traces the presence of a nonprompt baseline among different prompt categories ( Table 4 ), papers sources ( Table 5 ), and NLP tasks addressed ( Table 6 ).
Prompt category | No baseline, n (%) | Higher, n (%) | Similar, n (%) | Lower, n (%) | Total, n (%) |
Prompt design | 48 (42.1) | 13 (11.4) | 4 (3.5) | 13 (11.4) | 78 (68.4) |
Prompt learning | 5 (4.4) | 19 (16.7) | 3 (2.6) | 2 (1.8) | 29 (25.4) |
Prompt tuning | 3 (2.6) | 11 (9.6) | 2 (1.8) | 3 (2.6) | 19 (16.7) |
a Higher or lower indicates that the performance of the proposed prompt-based approach is higher or lower than the baseline.
Type of venue | No baseline, n (%) | Higher, n (%) | Similar, n (%) | Lower, n (%) | Total, n (%) |
Medical informatics | 13 (11.4) | 16 (14) | 2 (1.8) | 2 (1.8) | 33 (28.9) |
Computer science | 7 (6.1) | 12 (10.5) | 3 (2.6) | 9 (7.9) | 31 (27.2) |
Preprint | 21 (18.4) | 6 (5.3) | 1 (0.9) | 5 (4.4) | 33 (28.9) |
Clinical | 13 (11.4) | 0 (0) | 0 (0) | 0 (0) | 13 (11.4) |
Other | 1 (0.9) | 2 (1.8) | 0 (0) | 1 (0.9) | 4 (3.5) |
NLP task | No baseline, n (%) | Higher, n (%) | Similar, n (%) | Lower, n (%) | Total, n (%) |
Text classification | 13 (11.4) | 18 (15.8) | 4 (3.5) | 11 (9.6) | 46 (40.4) |
Question answering | 13 (11.4) | 3 (2.6) | 1 (0.9) | 2 (1.8) | 19 (16.7) |
Relation extraction | 3 (2.6) | 10 (8.8) | 0 (0) | 3 (2.6) | 16 (14) |
Information extraction | 10 (8.8) | 3 (2.6) | 0 (0) | 2 (1.8) | 15 (13.2) |
Multiple-choice question | 10 (8.8) | 3 (2.6) | 1 (0.9) | 1 (0.9) | 15 (13.2) |
Named entity recognition | 4 (3.5) | 5 (4.4) | 1 (0.9) | 5 (4.4) | 15 (13.2) |
Text summarization | 7 (6.1) | 3 (2.6) | 0 (0) | 1 (0.9) | 11 (9.6) |
Reasoning | 5 (4.4) | 3 (2.6) | 0 (0) | 1 (0.9) | 9 (7.9) |
Generation | 5 (4.4) | 2 (1.8) | 0 (0) | 1 (0.9) | 8 (7) |
Entity linking | 0 (0) | 3 (2.6) | 0 (0) | 0 (0) | 3 (2.6) |
Coreference resolution | 1 (0.9) | 1 (0.9) | 0 (0) | 1 (0.9) | 3 (2.6) |
Decision support | 2 (1.8) | 0 (0) | 0 (0) | 1 (0.9) | 3 (2.6) |
Conversational | 3 (2.6) | 0 (0) | 0 (0) | 0 (0) | 3 (2.6) |
Text simplification | 1 (0.9) | 0 (0) | 0 (0) | 1 (0.9) | 2 (1.8) |
a NLP: natural language processing.
b Higher or lower indicates that the performance of the proposed prompt-based approach is higher or lower than the baseline.
Nonprompt-related baselines are often featured in studies focused on PL and PT but not PD. Additionally, PL and PT have a tendency to perform better than their respective reported baselines, PD tends to report less conclusive results. More specifically, among the 22 papers using either PL or PT with an identical fine-tuned model as a baseline, 17 indicate superior performance with the prompt-based approach, 3 observed comparable performance, and 2 studies noted inferior performance.
Significantly, papers from computer science venues tend to include more state-of-the-art baselines than those from medical informatics and clinical venues. Specifically, all 13 papers reviewed from clinical venues did not use any nonprompt baselines. Furthermore, there appears to be no consistent link between the type of NLP tasks and the omission of baselines, indicating that the decision to include baselines is more influenced by the evaluation methodology than by feasibility.
Prompt Optimization
Numerous studies in the literature highlight the few-shot learning capabilities of LLMs, often referred to as “few-shot prompting,” wherein they demonstrate proficiency in executing tasks with minimal demonstrations provided, typically through text prompts. However, it is crucial to acknowledge that the annotation cost associated with such frameworks might extend beyond the few annotated demonstrations within the prompt. Many studies claiming to explore few-shot or zero-shot learning through prompt engineering rely on extensive annotated validation data sets to refine PD and formulation. This is, for example, the case in the paper that popularized the term “few-shot learning” [ 1 ]. Among the 45 analyzed papers concentrating on few-shot or zero-shot learning, 5 explicitly detail the optimization of prompt formulation using extensive validation data sets. Conversely, 18 of these papers either do not engage in prompt optimization or test various prompts and document all results. Notably, 22 papers present results using only 1 prompt choice, without clarifying whether this choice was made thanks to additional validation data sets.
Summary of the Findings
This scoping review aimed to map the current landscape of medical prompt engineering, identifying key themes, gaps, and trends within the existing literature. The primary findings of this study reveal a greater prevalence of PD over PL and PT, with ChatGPT dominating the PD domain. Additionally, many studies omit nonprompt-based baselines, do not specify the language of study, or exhibit a lack of consensus in PL (prefix vs cloze prompt) and PT settings (soft prompt lengths and positions). English is notably dominant as the language of study. These findings suggest that while the field is emerging, there is a pressing need for improved research practices.
Costs, Infrastructure, and LLMs in Clinical Settings
Prompt engineering techniques enable competitive performance in scenarios with limited or no resources as well as in environments with low-cost computing infrastructure. As hospital data and infrastructure are often found in this scenario, these approaches hold great promise in the clinical field. Figure 6 shows the absence of PL- and PT-related works in clinical journals. This trend may stem from the widespread accessibility of ChatGPT, favoring PD-focused investigations. Despite efforts like OpenPrompt [ 125 ] to facilitate PL and PT works, the programming barrier likely deters clinical practitioners. Surprisingly, 7 papers use ChatGPT with sensitive clinical data. Despite the recent availability of ChatGPT Enterprise in GPT-4 for secure data handling, it is apparent that most of these studies have not used this feature since they used GPT-3.5. Limited use of local LLMs, especially LLaMA-based, suggests a need for their increased adoption in future clinical PD studies. The lack of local LLMs may be due to clinicians’ limited computational infrastructure.
Prompt Engineering Techniques Effectiveness in Medical Research
In documented prompt engineering techniques, the effectiveness of few-shot prompting compared to zero shot varies by task and scenario. However, CoT shows superior reasoning performance, compelling LLMs to present reasoning pathways and consistently outperforming zero-shot and few-shot methods across PD studies. Its ensemble-based variant, self-consistency, consistently outperforms CoT. Despite the persona pattern’s frequent use, there is a lack of ablation studies on its impact on medical task performance, with only 1 paper reporting negligible improvement [ 61 ]. Prompt engineering is an emerging field of study that still needs to prove its efficacy. However, almost half of the papers focused only on prompt engineering and failed to report any nonprompt-related baseline performance, despite the availability of such baselines for the addressed NLP tasks. On the whole, the results are far from being systematically in favor of LLM-based methods, greatly attenuating the impression of a technological breakthrough that is generally commented on. Selecting a baseline remains a necessary step toward understanding the actual impact of prompt engineering.
Bender Rule
Regarding the languages, while Table 2 shows the dominance of English in medical literature, many papers studying English fail to explicitly mention the language of study. This oversight is more prevalent in computer science and clinical venues, whereas medical informatics exhibits a more favorable trend, as validated by a chi-square test yielding a P value of .02 (Table S1 in Multimedia Appendix 2 ). Notably, languages such as Chinese are consistently mentioned across the 18 selected papers. However, the Bender rule, namely “always name the language(s) you are working on,” seems to be well respected for languages other than English. This finding has already been documented for NLP research in general [ 126 ].
Fine-Tuning Versus Prompt-Based Approaches
While traditional LLM fine-tuning remains a viable method for various NLP tasks, PL and PT are competitive alternatives to fine-tuning, particularly in resource-constrained and low computational scenarios. PL, leveraging predefined prompts to guide model behavior, offers an efficient approach in low-to-no resource environments. Conversely, PT emerges as a viable solution in low computational scenarios, as it requires substantially fewer trainable parameters compared to traditional fine-tuning approaches. Since both prompt-based approaches do not require the LLM to be further trained, they are less prone to catastrophic forgetting [ 127 ].
Recommendations for Future Medical Prompt–Based Studies
For future research in prompt engineering, we propose several recommendations aimed at improving research quality, reporting, and reproducibility. From this review, we identified several trends such as the computational advantages or the lack of evaluations on baselines with a lack of ablation studies to evaluate the performance of the prompting strategies. Some studies do not clearly mention the prompt engineering choices they made. For instance, in PL, choices range from using cloze to prefix prompting and from using manual to soft verbalizer. Similarly, PT is characterized by configurations of soft prompts, such as the length and the positions. To clarify these distinctions and enhance methodological transparency and reproducibility in future research, we have developed reporting guidelines available in Textbox 1 . Adhering to these reporting guidelines will contribute to advancing prompt engineering methodologies and their practical applications in the medical field.
General reporting recommendations
- For sensitive data, local large language models (LLMs) should be preferred to the ones that use an application programming interface or a web service.
- The language of the study used should be explicitly stated.
- The mention of whether the LLM undergoes fine-tuning should be made explicit.
- The prompt optimization process and results should be documented to ensure transparency, whether it is through different tested manual prompts or through a validation data set.
- The terms “few-shot,” “one-shot,” and “zero-shot” should not be used in settings where the prompts have been optimized on annotated examples.
- Experiments should include baseline comparisons or at least mention existing results, particularly when data sets originate from previous medical challenges or benchmarks.
Specific to prompt learning and prompt tuning
- Concepts (such as prompt learning and prompt tuning) should be defined and used consistently with the consensus.
- In prompt learning experiments, the verbalizer used (soft and hard) should be explicitly specified, or a clear justification should be provided if the verbalizer is omitted. Additionally, whether the prompt template follows the cloze or the prefix format should be mentioned.
- In prompt tuning experiments, authors should provide details on soft prompt positions, length, and any variations tested, such as incorporating hard or mixed prompts, as part of the ablation study.
Limitations
A limitation was the large number of papers retrieved during the initial search, which was addressed by limiting the search scope to titles, abstracts, and keywords. Furthermore, since some studies may perform prompt engineering techniques without mentioning any of the 4 prompt-related expressions used in the queries, they might be missed by our searches.
Conclusions
Medical prompt engineering is an emerging field with significant potential for enhancing clinical applications, particularly in resource-constrained environments. Despite the promising capabilities demonstrated, there is a pressing need for standardized research practices and comprehensive reporting to ensure methodological transparency and reproducibility. Consistent evaluation against nonprompt-based baselines, prompt optimization documentation, and prompt settings reporting will be crucial for advancing the field. We hope that a better adherence to the recommended guidelines, in Textbox 1 , will improve our understanding of prompt engineering and enhance the capabilities of LLMs in health care.
Acknowledgments
JZ is financed by the NCCR Evolving Language, a National Centre of Competence in Research, funded by the Swiss National Science Foundation (grant # 51NF40_180888).
Authors' Contributions
JZ and MN performed the screening and data extraction of the papers and synthesized the findings. AN and XT supervised MN. MB and CL supervised JZ. JZ and MN wrote the manuscript with support from MB, AN, XT, and CL. All authors contributed to the analysis of the results. CL conceived the original idea.
Conflicts of Interest
CL is the editor-in-chief of JMIR Medical Informatics . All other authors have no conflict of interest to declare.
PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.
Search strategy and statistical analysis.
Reading notes and details of the reviewed papers.
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Abbreviations
Bidirectional Encoder Representations From Transformers |
chain-of-thought |
large language model |
multiple-choice question |
masked language modeling |
natural language processing |
prompt design |
prompt learning |
Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews |
prompt tuning |
Edited by T de Azevedo Cardoso; submitted 14.05.24; peer-reviewed by B Bhasuran, D Hu, A Jain; comments to author 03.07.24; revised version received 09.07.24; accepted 22.07.24; published 10.09.24.
©Jamil Zaghir, Marco Naguib, Mina Bjelogrlic, Aurélie Névéol, Xavier Tannier, Christian Lovis. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 10.09.2024.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research (ISSN 1438-8871), is properly cited. The complete bibliographic information, a link to the original publication on https://www.jmir.org/, as well as this copyright and license information must be included.
749 Medical Research Topics & Interesting Ideas
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Medical research topics encompass a wide range of critical areas in healthcare, including disease prevention, treatment, and understanding underlying mechanisms. They range from studying intricate molecular pathways to large-scale epidemiological surveys. Current hot topics include genomics, personalized medicine, and telehealth advancements driven by technological progress. In light of the recent pandemic, infectious disease research, including vaccine development, has garnered significant attention. Furthermore, the role of mental health in healthcare is being intensely studied. Research into healthcare disparities also addresses socioeconomic issues within medical practice. In essence, medical research topics embody the multidisciplinary and dynamic nature of modern health science.
Good Medical Research Topics
- Exploring the Impact of Nutrition on Cognitive Development in Children
- Roles of Telemedicine in Bridging the Health Care Gap in Rural Areas
- Genetic Markers and Their Significance in Personalized Medicine
- Effects of Climate Change on Vector-Borne Diseases
- Mental Health Consequences of Chronic Sleep Deprivation
- New Strategies for Organ Transplantation and Rejection Prevention
- Assessing the Long-Term Effects of Concussions on Professional Athletes
- Innovations in Artificial Intelligence for Early Disease Detection
- Holistic Approaches to Pain Management: The Future of Analgesics?
- Roles of Gut Microbiota in Obesity: The Next Frontier
- Ethical Dilemmas in Genome Editing Using CRISPR-Cas9 Technology
- Psychological Effects of Prolonged Isolation: Lessons From Space Travel
- Innovations in Prosthetics: The Intersection of Biology and Technology
- Harnessing Stem Cells for Regenerative Medicine: Current Progress and Challenges
- Autism Spectrum Disorders: Advances in Early Diagnosis and Treatment
- Nanobiotechnology: Unraveling New Horizons in Cancer Treatment
- Influence of Social Media on Adolescent Mental Health
- Cellular Senescence: Unlocking the Secrets of Aging
- The Role of Epigenetics in Neurodegenerative Disorders
- Vaping and E-Cigarettes: A New Generation of Nicotine Addiction
- Advancements in Non-Invasive Brain Stimulation Techniques for Mental Disorders
Easy Medical Research Topics
- Climate Change and Its Impact on Respiratory Health
- The Role of Hygiene Hypothesis in Allergic Diseases
- Tick-Borne Diseases in the Era of Global Warming
- Cellular Mechanisms in Heart Disease: An Update
- Microplastics: An Emerging Threat to Human Health
- Advancements in Gene Therapy for Hemophilia
- The Impact of Antibiotic Resistance on Global Health
- Effectiveness of Virtual Reality in Pain Management
- Innovations in Preventing Maternal Mortality in Developing Countries
- The Role of Microbiome in Immune System Regulation
- Unveiling the Mysteries of the Brain: The Promise of Connectomics
- Advances in 3D Bioprinting for Tissue Engineering
- Evolution of HIV/AIDS Treatment: From AZT to Modern Therapies
- Understanding the Biochemistry of Addiction
- Links Between Sedentary Lifestyle and Mental Health Disorders
- The Impact of Animal-Assisted Therapy on Mental Health
- AI and Machine Learning in Predictive Health Analytics
- Roles of Meditation in Stress Management: A Neurobiological Perspective
- Chronic Inflammation and Its Impact on Cardiovascular Health
- Therapeutic Potential of Peptides in Neurodegenerative Diseases
- Impacts of Air Pollution on Cognitive Function
- Molecular Mechanisms Behind Gluten Intolerance
Interesting Medical Research Topics
- Application of Machine Learning in Radiology: Progress and Prospects
- Understanding Neuromuscular Diseases: Current Research and Future Directions
- Telehealth During Pandemics: Lessons From COVID-19
- Health Risks Associated With Prolonged Exposure to Blue Light
- Biodegradable Implants and Their Impact on Orthopedic Medicine
- Harnessing Big Data for Public Health Improvements
- Role of Endocrine Disruptors in Obesity Epidemic
- Potential Therapeutic Use of Cannabinoids in Neurological Disorders
- Regulating CRISPR Gene-Editing: Ethical and Legal Issues
- Exploring Links Between Gut Health and Mental Health
- Understanding Molecular Mechanisms of Autoimmune Disorders
- Unraveling the Complexities of Chronic Fatigue Syndrome
- The Impact of Music Therapy on Neurological Disorders
- Roles of Oxidative Stress in Aging and Disease
- Therapeutic Potential of Ketogenic Diets in Neurodegenerative Disorders
- The Influence of Green Spaces on Public Health
- Nanoparticles in Drug Delivery: Advancements and Challenges
- Biological Clocks and Their Role in Disease Processes
- The Promise of Liquid Biopsy in Cancer Detection and Management
- Nutritional Psychiatry: Understanding the Diet-Mental Health Connection
Medical Research Paper Topics for High School
- Understanding the Role of Genetics in Neurological Disorders
- Effects of Digital Screen Exposure on Adolescent Mental Health
- Investigating the Link Between Obesity and Type 2 Diabetes
- Progress in Personalized Medicine: Prospects and Challenges
- Evaluating the Impact of Vaccination Programs on Public Health
- Connections Between Sleep Deprivation and Academic Performance
- Roles of Artificial Intelligence in Modern Medical Diagnosis
- Ethical Concerns Surrounding Genetic Engineering and Human Cloning
- Influence of Climate Change on Disease-Spreading Patterns
- Autism Spectrum Disorder: Genetic or Environmental Origin?
- Implications of Antibiotic Resistance for Global Health
- Nutritional Therapy’s Effectiveness in Chronic Disease Management
- Stress Management Techniques: Their Impact on Student Health
- Evolution of Telemedicine and Its Potential Benefits
- Examination of the Microbiome’s Influence on Human Health
- Depression in Adolescents: Causes and Prevention Strategies
- Nanotechnology in Medicine: Potential Applications and Ethical Concerns
- Exploring the Psychosocial Impact of Chronic Diseases
- Exercise’s Roles in the Prevention and Treatment of Cardiovascular Diseases
- Understanding the Science Behind Addiction: Drugs and Behavior
Medical Research Paper Topics for College Students
- Exploring the Psychological Effects of Long-Term Solitude
- Innovations in Cancer Treatment: Targeting Tumors Precisely
- Identifying Genetic Markers in the Evolution of Alzheimer’s Disease
- Unraveling the Complexities of Mental Health in the LGBT+ Community
- Childhood Obesity: Measures for Prevention and Control
- Studying the Impact of Stress on Cardiovascular Health
- Neuroplasticity: Unveiling the Brain’s Ability to Adapt
- Autism Spectrum Disorders: Evaluating Early Intervention Strategies
- Potential Therapies for Parkinson’s Disease: The Road Ahead
- Unearthing Causes and Treatments of Rare Genetic Disorders
- Nanotechnology: Prospects for Disease Diagnosis and Treatment
- Depression and Anxiety: A Comparative Study on Treatment Methods
- Sleep Disorders: Understanding the Role of Melatonin
- Inflammatory Bowel Disease: Evaluating Probiotics as a Treatment Option
- Dissecting the Role of Epigenetics in Cancer Development
- Opioid Crisis: Proposing Effective Intervention Strategies
- Pioneering Techniques in Organ Transplantation and Regeneration
- Vaccines and Autism: Debunking Myths and Misinformation
- Effects of Diet on Gut Microbiome and Overall Health
- Unfolding Links Between Mental Health and Gut Microbiome
- The Influence of Genomics on Personalized Medicine
Medical Research Paper Topics for University
- Stem Cells: Potential in Regenerative Medicine
- Telemedicine: Examining Its Effectiveness in Rural Healthcare
- Occupational Health Risks Associated with Nanotechnology
- Advances in HIV/AIDS Research: Prospects for a Cure
- Examine Bioethics in Genetically Modified Organisms
- Machine Learning in Medical Diagnosis: Promises and Challenges
- Antimicrobial Resistance: Searching for New Antibiotics
- Health Disparities: A Closer Look at Socioeconomic Factors
- Addiction and Recovery: Analyzing Treatment Modalities
- Zoonotic Diseases: Understanding the Human-Animal Disease Interface
- Advances in Neuroimaging Techniques for Mental Disorders
- Roles of Traditional Medicine in Modern Healthcare
- Unpacking the Physiological Impacts of Aging
- Trauma-Informed Care: Revolutionizing Mental Health Treatment
- Health Consequences of Electronic Cigarettes and Vaping
- Telehealth Innovations: Paving the Way for Accessible Healthcare
- Advances in Non-Invasive Surgical Techniques
- Post-Traumatic Stress Disorder: Coping Strategies and Treatments
- Mapping the Human Brain: Progress in Neuroimaging
- Deconstructing Myths about Vaccines: An Educational Approach
- Epidemics and Pandemics: Lessons Learned From History
- Medical Robotics: The Future of Surgery
Medical Research Paper Topics for Master’s
- Exploring the Long-Term Effects of Childhood Obesity on Adult Health
- Advances in Regenerative Medicine: Current Practices and Future Potential
- Mental Health Implications of Chronic Pain Management
- Machine Learning Applications in Medical Diagnostics
- Genetic Factors in Cardiovascular Disease: A Study of Hereditary Risk
- Psychological Impacts of Terminal Illness Diagnosis on Patients
- Developments in Telemedicine: Effectiveness in Rural and Remote Health Care
- Epigenetic Influences on Disease Manifestation and Progression
- Potential of Nanotechnology in Cancer Treatment
- Alzheimer’s Disease: Exploring New Frontiers in Treatment Approaches
- Opioid Addiction: Overcoming the Challenges in Treatment and Rehabilitation
- Effectiveness of Alternative Medicine in Chronic Illness Management
- Technological Innovations in Prosthetic Devices: Enhancing the Quality of Life
- Analysis of Genetic Modification Techniques in Curing Inherited Diseases
- Clinical Trials: Ethical Considerations and Transparency
- Pandemics and Global Health: Learning From COVID-19
- Autism Spectrum Disorder: Early Detection and Intervention Strategies
- Vaccine Development: A Study in Speed, Efficiency, and Safety
- Nutritional Psychiatry: Food as an Intervention in Mental Health
- Stress and Its Impact on Cardiovascular Health
- Pathophysiology of Autoimmune Diseases: An In-Depth Study
Medical Research Paper Topics for Ph.D.
- Roles of Virtual Reality in Physical Rehabilitation
- Artificial Intelligence in Predicting and Managing Epidemics
- Precision Medicine: The Future of Individualized Treatment
- DNA Sequencing Technologies: Impact on Personalized Medicine
- Climate Change and Its Influence on Vector-Borne Diseases
- Investigating the Links Between Chronic Sleep Deprivation and Diseases
- Epigenetics and Aging: The Potential for Reversal
- Bioethical Dilemmas in Gene Editing Technologies
- Exploration of Stem Cell Therapies for Degenerative Diseases
- Technological Advancements in Hearing Aid Devices
- Neuroplasticity and Its Implications for Stroke Rehabilitation
- Environmental Factors Contributing to Asthma Prevalence
- Bionic Vision: Emerging Prospects and Challenges
- Non-Invasive Brain Stimulation Techniques in Treating Depression
- Microplastics in Human Health: Hidden Threats and Possible Solutions
- Roles of Genomics in Predicting Disease Susceptibility
- Influence of Biomedical Engineering on Patient Care
- Emerging Antibiotic Resistance: Strategies for Mitigation
- Clinical Implications of Artificial Organs: A Futuristic Study
- Dementia: Investigating New Prevention Strategies
- Neonatal Care: Advances and Challenges in Premature Birth Management
Medical Research Paper Topics About COVID-19
- The Efficacy of COVID-19 Vaccines in Controlling the Disease
- Long-Term Effects of COVID-19 on Respiratory Health
- Impacts of COVID-19 on Mental Health in Healthcare Workers
- Antiviral Medications: Their Role in COVID-19 Treatment
- Transmission Dynamics of COVID-19 Variants: An Analysis
- Effectiveness of Social Distancing Measures in Reducing COVID-19 Spread
- The Relationship Between COVID-19 and Cardiovascular Complications
- Effectiveness of COVID-19 Testing Strategies in Identifying Cases
- Immunological Response to COVID-19 Infection: An Exploration
- Impacts of COVID-19 on Pediatric Populations: An Assessment
- Inflammation and COVID-19 Severity: An Investigation
- Efficacy of Monoclonal Antibodies in COVID-19 Treatment
- COVID-19 and Pregnancy: Examining Maternal Health
- Roles of T-Cell Immunity in COVID-19 Recovery: An Analysis
- Effectiveness of Contact Tracing in Controlling COVID-19 Outbreaks
- Impacts of COVID-19 on Economic and Social Disparities: An Evaluation
- Genetic Factors Influencing COVID-19 Susceptibility: An Exploration
- Effectiveness of COVID-19 Vaccines in Older Adults: An Assessment
- COVID-19 and the Renal System: Analyzing the Impact
- Cytokine Storm and COVID-19 Pathogenesis: Investigating the Role
- Effects of COVID-19 on Olfactory and Gustatory Functions: An Analysis
Controversial Medical Research Paper Topics
- Ethical Implications of Human Cloning: A Medical Perspective
- Brain-Computer Interfaces: Potential Benefits and Risks
- Examination of Genetic Engineering and Its Impact on Human Disease
- Euthanasia Policies: Comparisons Across Different Countries
- Stem Cell Research: Miracle Cure or Ethical Dilemma?
- Prolonging Life With Artificial Intelligence: Possibilities and Problems
- Animal Experimentation in Drug Discovery: Justifiable or Not?
- Vaccination Controversies: Assessing the Anti-Vax Movement
- Nanotechnology in Medicine: Exciting Innovation or Unforeseen Danger?
- The Influence of Pharmaceutical Companies on Medical Research
- The Controversy Surrounding the Use of Medical Marijuana
- Artificial Organ Transplants: Future Prospects and Ethical Challenges
- Exploring the Implications of CRISPR Gene Editing Technology
- Assisted Reproductive Technologies: Moral and Ethical Considerations
- Disparities in Medical Treatment: Examining Racial and Socioeconomic Factors
- Therapeutic Use of Psychedelics: A New Frontier in Psychiatry?
- Telemedicine and Patient Privacy: Potential Breaches and Solutions
- Genetically Modified Foods: Health Marvel or Risky Business?
- Mental Health Stigma in Healthcare: Addressing the Issue
- Bioethics of Human Enhancement Technologies
Medical Research Paper Topics About Health
- Impacts of Telemedicine on Rural Healthcare Accessibility
- Roles of Artificial Intelligence in Predicting Disease Outbreaks
- Examination of Genomic Medicine in Personalized Care
- Comparative Analysis of Western and Traditional Chinese Medicine
- Utilizing Virtual Reality for Pain Management Therapy
- Exploring Mental Health Benefits of Pet Therapy
- Nutritional Genomics: Understanding the Diet-Gene Relationship
- Vaccination Policies and Their Impact on Public Health
- Aging Population and the Challenge of Geriatric Care
- Microbiome Research: Its Impact on Digestive Health
- Analyzing Nanotechnology Applications in Cancer Therapy
- Influence of Sleep Disorders on Mental Health
- Roles of Epigenetics in Understanding Human Disease
- Utilizing Big Data in Health Research and Epidemiology
- Impacts of Socioeconomic Factors on Community Health
- Ethical Dilemmas in Genetic Engineering and Medicine
- Advances in 3D Printing for Organ Transplants
- Potential of Psychedelic Drugs in Treating Mental Disorders
Medical Research Paper Topics About Medicine
- Revolutionizing Healthcare: The Potential of Artificial Intelligence
- Unlocking the Mystery of the Human Microbiome
- Advanced Gene Therapy: A Promising Treatment for Genetic Disorders
- Stem Cell Research: Ethical Challenges and Medical Breakthroughs
- Mental Health Stigma: Societal Perceptions and Implications
- Mitigating the Impact of Antibiotic Resistance
- Pain Management: Exploration of Non-pharmacological Methods
- New Horizons in Cancer Immunotherapy
- Early Detection Strategies for Alzheimer’s Disease
- Precision Medicine: Tailoring Treatment to Individual Genomes
- Examining the Health Effects of Climate Change
- Future of Telemedicine: Accessibility and Implications
- Advancements in Prosthetics: Improving Mobility for Amputees
- Impacts of Obesity on Cardiovascular Health
- Innovations in Neonatal Care: Reducing Infant Mortality Rates
- Potential Risks and Benefits of CRISPR Cas-9 Technology
- Role of Sleep in Maintaining Mental Health
- Exploring the Neuroscience of Addiction
- Virtual Reality Applications in Pain Management
- Immunological Insights into Autoimmune Diseases
- Breakthroughs in Regenerative Medicine: A Look at Organ Bioengineering
- Neuroplasticity: How Learning and Experience Change the Brain
Healthcare Research Paper Topics
- Mitigating the Rise of Antibiotic Resistance in Healthcare
- Emergence of Personalized Nutrition for Chronic Disease Prevention
- Understanding the Psychosocial Impact of Chronic Illnesses
- Interplay between Mental Health and Cardiovascular Diseases
- Obesity Epidemic: Biological and Social Factors
- Developments in Immunotherapy for Autoimmune Disorders
- Future of Bioinformatics in Personalized Medicine
- Roles of Social Media in Healthcare Communication
- Physical Activity and Its Impact on Cognitive Function
- Importance of Health Literacy in Disease Prevention
- Effects of Urbanization on Public Health
- Advances in Neuroprosthetics: A Revolution in Rehabilitation
- Impacts of Prenatal Exposure to Environmental Toxins
- Connections Between Gut Health and Neurological Disorders
- Investigation of the Human Virome and Disease
- Maternal Health: Disparities and Strategies for Improvement
- Genetic Basis of Rare Pediatric Diseases
- Promoting Mental Health in the Workplace
- Innovations in Wound Healing and Regenerative Medicine
- Exploration of Bioethical Issues in Assisted Reproduction
- Combatting the Global Impact of Neglected Tropical Diseases
- The Role of Microplastics in Human Health
Public Health Research Paper Topics
- Analysis of Obesity as a Public Health Concern
- Influence of Mental Health on Community Well-Being
- Roles of Vaccinations in Global Health Security
- Impacts of Urbanization on Public Health
- Advancements in HIV/AIDS Prevention Strategies
- Environmental Factors and Asthma Prevalence
- Assessing Health Disparities in Minority Communities
- Childhood Vaccination Rates and Public Health Outcomes
- Implications of Substance Abuse on Public Health
- Chronic Disease Management in Low-Income Populations
- Studying Effects of Air Pollution on Respiratory Diseases
- Public Health Response to Zoonotic Diseases
- Evaluating Food Insecurity as a Public Health Issue
- Importance of Clean Water in Disease Prevention
- Impacts of Climate Change on Vector-Borne Diseases
- Maternal and Child Health in Developing Nations
- Advancing Tobacco Control for Public Health Promotion
- Occupational Health Hazards and Their Public Implications
- Aging Population and Its Impact on Public Health Services
- Influence of Physical Activity on Chronic Disease Prevention
Mental Health Research Paper Topics
- Correlation Between Childhood Trauma and Adult Anxiety Disorders
- Digital Age: Effects of Social Media on Adolescents’ Mental Health
- Treatment Efficacy of Cognitive Behavioral Therapy in Depression Management
- Interplay of Genetics and Environment in Bipolar Disorder
- Integration of Yoga and Mindfulness Practices in Psychotherapy
- Roles of Nutrition in Optimizing Mental Health Outcomes
- Machine Learning in Predicting Mental Health Disorders
- Deciphering the Neurobiological Underpinnings of Schizophrenia
- Post-Traumatic Stress Disorder: Advances in Diagnostic Techniques
- Connection Between Cardiovascular Diseases and Mental Health
- Art Therapy: Analyzing Its Effectiveness in Mental Health Rehabilitation
- De-Stigmatizing Mental Health: Strategies and Policies
- Neurofeedback and its Application in Attention Deficit Hyperactivity Disorder
- Efficacy of Virtual Reality Exposure Therapy in Phobia Treatment
- Holistic Approaches to Mental Health: The Role of Physical Exercise
- Exploring the Links Between Climate Change and Mental Health
- Epigenetics and Its Influence on Mental Health Disorders
- Implications of Sleep Disorders on Mental Health
- Telemedicine and Remote Therapy: Their Efficacy in Mental Health Treatment
- Neuroplasticity: The Brain’s Capacity to Recover From Mental Health Disorders
- The Potential of Psychedelics in Treating Severe Mental Health Conditions
Medical Anatomy Research Paper Topics
- Advances in Microscopic Analysis of Human Cells
- Roles of Stem Cells in Tissue Regeneration
- Genetic Influences on Skeletal Development
- Neuroanatomy: The Complexity of the Human Brain
- Vascular System: Understanding Diseases and Treatments
- Ocular Anatomy: Progress in Retinal Disease Research
- Musculoskeletal System: Exploring New Treatments for Osteoporosis
- Innovation in Transplantation: A Look at Organ Engineering
- Human Genetics: Tracing Ancestry through Mitochondrial DNA
- Decoding the Human Immune System: The Anatomy of Defense
- Modern Techniques in Anatomical Imaging
- Evolutionary Influences on Human Anatomy
- Gut Microbiota and Its Role in Digestive Health
- The Human Heart: Anatomical Changes During Cardiovascular Disease
- Skin: The Body’s First Line of Defense
- Roles of Epigenetics in Organ Development and Function
- Nervous System: Insights into Neural Regeneration
- Respiratory System: New Insights Into Asthma and Allergies
- Exploring the Interplay Between Anatomy and Psychology
- The Complexity of the Endocrine System: Understanding Hormonal Imbalances
Biomedical Research Paper Topics
- Exploring the Role of Genomics in Personalized Medicine
- Impacts of Biomedical Nanotechnology on Drug Delivery
- Neurobiological Insights into Mental Health Disorders
- Biomechanics and Its Applications in Orthopedic Treatments
- Unraveling the Mystery of Telomeres in Aging and Disease
- Regenerative Medicine: Stem Cell Therapies and Beyond
- Pioneering Research in Biomedical Imaging Techniques
- Genetic Markers: Decoding Their Role in Predicting Disease
- Harnessing the Power of Proteomics for Disease Detection
- Metabolomics: A New Frontier in Biomedical Research
- Tissue Engineering: A Biomedical Solution for Organ Transplants
- Understanding the Biomedical Implications of Epigenetics
- Nanobots: The Future of Minimally Invasive Surgery?
- Roles of Biomedical Informatics in Healthcare Improvement
- Importance of Translational Medicine in Clinical Applications
- Genetic Engineering: Unleashing the Potential for Disease Eradication
- Advances in Medical Robotics and Their Biomedical Implications
- Biocompatibility of Implant Materials: A Biomedical Analysis
- Bioethics in Biomedical Research: Navigating the Gray Areas
- Unfolding the Role of Exosomes in Cell-Cell Communication
Medical Research Paper Topics on Bioethics
- Ethical Implications of Gene Editing and Designer Babies
- The Balance Between Medical Innovation and Human Experimentation
- Investigating the Bioethics of End-of-Life Care: Right to Die or Duty to Live?
- Cloning Technologies: A Journey Into Bioethical Boundaries
- Mandatory Vaccinations: A Crossroads of Public Health and Personal Freedom
- Equity in Health Care: The Ethics of Allocating Resources
- Animal Testing: Weighing Scientific Progress Against Animal Rights
- Ethical Dilemmas in Organ Transplantation: Consent, Allocation, and Transplant Tourism
- Balancing Autonomy and Beneficence in Patient Care: Who Decides?
- Ethics Behind Prenatal Genetic Diagnosis: Where Do We Draw the Line?
- Consent in Medical Research: Assessing the Ethics of Deception
- The Bioethics of Artificial Intelligence in Health Care
- Truth-Telling in Medicine: The Ethical Boundaries of Patient Information
- Ethical Considerations in Pediatric Medicine: Protecting Minors’ Rights
- Biobanks and Genetic Privacy: The Ethics of DNA Data Sharing
- Medical Practitioner Burnout: An Ethical Examination of Workload and Patient Safety
- Human Enhancement Technologies: Exploring the Ethics of Biological Superiority
- Mental Health and Bioethics: The Ethics of Involuntary Treatment
- Medical Refugees: A Bioethical Perspective on Cross-Border Care
- Stem Cell Research: Bioethical Questions Around Source and Use
- Ethical Challenges of Antimicrobial Resistance: Balancing Treatment and Prevention
- Genetic Counselling: Examining the Bioethical Implications of Predictive Medicine
Medical Research Paper Topics on Cancer
- Unveiling the Genetic Pathways Involved in Breast Cancer
- Understanding Pancreatic Cancer: Current Research and Future Directions
- Implications of Lifestyle Modifications on Prostate Cancer Risk
- The Impact of Nutrition on Colorectal Cancer Incidence and Progression
- Innovative Immunotherapy Approaches in the Treatment of Lung Cancer
- Liquid Biopsy: A Revolutionary Tool for Early Detection of Cancer
- Pediatric Oncology: Unique Challenges in Childhood Leukemia
- Nanotechnology’s Role in Precision Medicine for Cancer Treatment
- Environmental Factors Contribute to Skin Cancer: An Epidemiological Study
- Chemotherapy Resistance in Ovarian Cancer: Mechanisms and Solutions
- Metastasis Mystery: The Complex Journey of Cancer Cells
- Advancements in Radiology: Increasing Effectiveness in Brain Cancer Treatment
- Investigating the Role of Epigenetics in Oral Cancer
- Mental Health Effects of Cancer Diagnosis and Treatment
- The Evolution of Surgical Techniques in the Management of Thyroid Cancer
- Immunomodulatory Effects of Gut Microbiota on Colorectal Cancer
- Harnessing the Power of Stem Cell Research in Leukemia Treatment
- Genetic Profiling: A Key to Personalized Breast Cancer Treatment
- Esophageal Cancer and the Impact of Alcohol Consumption: A Comprehensive Review
- The Interplay of Diet and Genetics in Gastric Cancer
Clinical Research Paper Topics
- Unraveling the Impact of Antibiotic Resistance in Modern Healthcare
- Investigation into Personalized Medicine and Its Potentials in Treating Cancer
- Analysis of Cellular Mechanisms in Neurodegenerative Diseases
- Overcoming Challenges in Pediatric Clinical Trials: Ensuring Ethical Practices
- Interpreting Psychiatric Clinical Trials: Focus on Antidepressant Efficacy
- Diving Deep into Genetic Counseling and its Role in Clinical Studies
- Exploring the Impact of AI and Machine Learning on Clinical Trials
- Unpacking the Complexities of Vaccine Development: A Case Study on COVID-19
- Understanding the Role of Placebo Control Groups in Clinical Research
- Evaluating the Effects of Diet and Nutrition on Chronic Diseases
- Pioneering Breakthroughs in Gene Therapy Clinical Trials
- Advancing Pain Management: Clinical Trials on Non-Opioid Analgesics
- Roles of Telemedicine in Conducting Remote Clinical Trials
- Immune Checkpoint Inhibitors: Revolutionizing Oncology Clinical Trials
- Dissecting the Clinical Trials Process: An Overview of Phases I-IV
- Bridging the Gap: Integrating Traditional Medicine in Clinical Research
- Precision Medicine in Cardiovascular Diseases: A Clinical Perspective
- Stem Cell Therapy: Progress and Challenges in Clinical Trials
- Controversies Surrounding Experimental Drug Use in Clinical Studies
- Harnessing Big Data for Clinical Trials: Possibilities and Pitfalls
- Ethical Dilemmas in Clinical Research: Case Studies and Lessons Learned
Critical Care Research Paper Topics
- The Role of High-Frequency Oscillatory Ventilation in Critical Care Management
- Effectiveness of Targeted Temperature Management in Post-Cardiac Arrest Care
- Utilizing Artificial Intelligence for Early Detection of Sepsis in Critical Care Patients
- Impacts of Telemedicine on the Delivery of Critical Care Services in Rural Areas
- Novel Approaches in Pain Management for Critically Ill Patients
- Investigating the Efficacy of Probiotics in Preventing Ventilator-Associated Pneumonia
- The Role of Biomarkers in Predicting the Outcome of Acute Respiratory Distress Syndrome (ARDS)
- Exploring the Benefits of Multidisciplinary Team Approach in Critical Care Decision-Making
- Enhancing Communication and Collaboration in Interprofessional Critical Care Teams
- Optimizing Nutrition Support for Critically Ill Patients: Current Challenges and Future Directions
- Assessing the Effectiveness of Early Mobilization in Preventing Ventilator-Associated Pneumonia
- The Role of Blood Transfusions in the Management of Critically Ill Patients
- Investigating the Impact of Music Therapy on Pain and Anxiety
- Exploring the Use of Point-of-Care Ultrasound in Critical Care Settings
- The Influence of Gender on Critical Care Outcomes: An In-Depth Analysis
- Examining the Effectiveness of Non-Invasive Ventilation in Acute Respiratory Failure
- The Role of Antibiotic Stewardship Programs in Reducing Antibiotic Resistance in Critical Care Units
- Novel Approaches in Delirium Management for Intensive Care Patients
- Exploring the Benefits of Family-Centered Care in the Intensive Care Unit (ICU) Setting
- Evaluating the Use of Continuous Renal Replacement Therapy (CRRT) in Critically Ill Patients
Pediatric Research Paper Topics
- Investigating the Impact of Childhood Obesity on Cognitive Development
- Analyzing the Efficacy of Vaccination Programs in Pediatric Populations
- Examining the Role of Genetics in Childhood Asthma
- Evaluating the Effectiveness of Early Intervention Programs for Autism Spectrum Disorder
- Assessing the Long-Term Effects of Premature Birth on Neurodevelopmental Outcomes
- Exploring Innovative Therapies for Pediatric Cancer Treatment
- Investigating the Relationship Between Sleep Disorders and Academic Performance in Children
- Analyzing the Psychological Impact of Bullying on Pediatric Mental Health
- Evaluating the Effectiveness of Pharmacological Interventions for Attention-Deficit/Hyperactivity Disorder (ADHD)
- Examining the Role of Nutrition in Promoting Healthy Growth and Development in Children
- Assessing the Efficacy of Telemedicine in Delivering Pediatric Healthcare Services
- Analyzing the Psychological Effects of Childhood Trauma on Emotional Well-being
- Exploring the Role of Physical Activity in Reducing the Risk of Chronic Diseases in Children
- Investigating the Impact of Screen Time on Cognitive Development in Pediatric Populations
- Examining the Relationship Between Gut Microbiota and Pediatric Digestive Disorders
- Analyzing the Genetic Basis of Rare Pediatric Diseases
- Investigating the Role of Music Therapy in Managing Pain and Anxiety in Pediatric Patients
- Assessing the Effectiveness of Early Intervention Programs for Developmental Delays in Children
- Exploring the Impact of Air Pollution on Pediatric Respiratory Health
- Evaluating the Efficacy of Non-Pharmacological Interventions for Pediatric Sleep Disorders
Dental Research Paper Topics
- Oral Microbiome and Its Role in Dental Health
- Impacts of Fluoride on Preventing Dental Caries
- Innovative Techniques for Tooth Regeneration
- Advancements in Dental Implant Technology
- Effective Management of Dental Pain in Pediatric Patients
- Roles of Genetics in Dental Anomalies and Disorders
- Novel Approaches for Dental Anxiety and Phobia Management
- The Impact of Nutrition on Oral Health
- Emerging Trends in Orthodontic Treatment
- Analysis of Dental Materials and Their Biocompatibility
- Oral Cancer: Diagnosis, Treatment, and Prevention
- Role of Stem Cells in Dental Tissue Engineering
- Assessment and Management of Temporomandibular Disorders
- Dental Caries Risk Assessment and Prevention Strategies
- Exploring the Benefits of Laser Dentistry
- Advancements in Digital Dentistry: 3D Printing and CAD/CAM Technology
- Dental Trauma: Evaluation and Treatment Modalities
- The Relationship Between Diabetes and Periodontal Disease
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Clinical hypnosis vs. cognitive behavioral therapy: What's better for managing hot flashes?
by The North American Menopause Society
Nonhormone options for hot flashes and other menopause symptoms are growing in popularity, especially for women who cannot take hormones due to health complications. Cognitive behavioral therapy and clinical hypnosis are common nonhormone treatment options. According to a new scoping review, however, one is more effective than the other.
Results of the scoping review are presented at the 2024 Annual Meeting of The Menopause Society , held in Chicago from September 10–14.
Recognizing that a percentage of menopausal women cannot take hormone therapy either because of health restrictions, such as being a breast cancer survivor, or because of their concerns regarding the potential risks of hormones, in 2023 The Menopause Society published its Nonhormone Therapy Position Statement. Among other things, the Position Statement addressed both cognitive behavioral therapy and clinical hypnosis .
A new scoping review which synthesized the findings from 23 studies spanning from 1996 until 2022, however, was designed to compare the effectiveness of these two treatment options. Of the total studies reviewed, eight had administered clinical hypnosis and 15 administered cognitive behavioral therapy for the treatment of hot flashes.
The researchers found that clinical hypnosis interventions consistently demonstrated clinically significant efficacy in reducing hot flash frequency and severity, as well as improving quality of life, sleep quality, and mood. Specifically, clinical hypnosis showed a significant reduction of more than 60%.
In contrast, cognitive behavioral therapy interventions showed mixed findings, with minimal impact on hot flash frequency reduction, although they did prove helpful in reducing the daily interference and stress associated with hot flashes.
"Clinical hypnosis is the first behavioral intervention to achieve significant reductions of physiologically recorded hot flashes," says Vanessa Muniz, lead author from Baylor University.
"This suggests that hypnosis may act through mechanisms beyond response expectancy or placebo effects, potentially altering activity in the medial preoptic area of the hypothalamus."
Based on the results, the researchers suggest that future research should explore neurophysiological mechanisms of hypnosis and innovative delivery methods such as smartphone apps, and tailor interventions to individual characteristics for optimized outcomes in managing hot flashes.
"Since hot flashes are one of the most common bothersome symptoms of menopause, evaluating the available treatment options , including the nonhormone options, are important so we can provide our patients with the option that will work best for them," says Dr. Stephanie Faubion, medical director for The Menopause Society.
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Working together, we can reimagine medicine to improve and extend people’s lives.
Principal Data Scientist Imaging and Vision AI Researcher
About the role.
Key responsibilities
- Lead AI research projects independently, focusing on imaging and vision applications within pharmaceutical research and development using scientific, rigorous, and reproducible methodology
- Develop and implement state-of-the-art AI algorithms for image processing, analysis, segmentation, and interpretation, focusing on data modalities such as MRI, histopathology, echocardiograms, lab animal video data or other image modalities used in biological research.
- Collaborate with other AI specialists, data architects, software engineers, business analysts, user interface designers within RX on products in the Image/Vision domain
- Act as an Imaging and data science subject matter expert, to help commodify AI and data analytics expertise and assets into impactful analysis workflows and software products, and to help make our research data FAIR
- Engage with diverse stakeholders in clinical as well as non-clinical settings
- Serve as an ambassador for AI/Data Science by presenting and publishing articles.
- Build a bridge between academia, technology, and industry partners.
- Keep ahead of latest developments in the field and mentor associates .
- Be a part of a truly unique organization, work with an inter-disciplinary team of highly accomplished scientists and push the AI frontiers for disease understanding and drug discovery.
This opportunity is located at the Novartis Cambridge (MA) site and will not have the ability to be located remotely. Relocation assistance may be available.
The pay range for this position at commencement of employment is expected to be between $144,000 and $216,000 per year; however, while salary ranges are effective from 1/1/24 through 12/31/24, fluctuations in the job market may necessitate adjustments to pay ranges during this period. Further, final pay determinations will depend on various factors, including, but not limited to geographical location, experience level, knowledge, skills and abilities. The total compensation package for this position may also include other elements, including a sign-on bonus, restricted stock units, and discretionary awards in addition to a full range of medical, financial, and/or other benefits (including 401(k) eligibility and various paid time off benefits, such as vacation, sick time, and parental leave), dependent on the position offered. Details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at-will position” and the Company reserves the right to modify base salary (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.
The Novartis Group of Companies are Equal Opportunity Employers and take pride in maintaining a diverse environment. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to building diverse teams, representative of the patients and communities we serve, and we strive to create an inclusive workplace that cultivates bold innovation through collaboration and empowers our people to unleash their full potential.
Role Requirements
- Ph.D. in Computer Science, AI, Machine Learning, or a related field with a focus on imaging and vision applications.
- Minimum of 5 y ears of experience in AI research, specifically in imaging and vision applications.
- Applied knowledge of image/vision data modalities used in biological research or clinical settings
- Minimum of 4 y ears of relevant publication track record in machine learning applications and AI/ML methods
- Proven experience in leading AI research projects independently.
- Strong understanding of advanced machine learning algorithms and AI systems.
- Extensive experience in using AI for image analysis, particularly in a pharmaceutical setting.
- Proficient in programming in AI platforms such as TensorFlow, PyTorch, or Keras.
- Strong ability to communicate technical concepts to a variety of audiences, including scientists, engineers, and non-technical stakeholders.
- Familiarity with pharmaceutical research processes and requirements.
- Strong problem-solving skills and the ability to think creatively and innovatively.
Why Novartis? 766 million lives were touched by Novartis medicines in 2021, and while we’re proud of this, we know there is so much more we could do to help improve and extend people’s lives. We believe new insights, perspectives and ground-breaking solutions can be found at the intersection of medical science and digital innovation. That a diverse, equitable and inclusive environment inspires new ways of working. We believe our potential can thrive and grow in an unbossed culture underpinned by integrity, curiosity and flexibility. And we can reinvent what's possible, when we collaborate with courage to aggressively and ambitiously tackle the world’s toughest medical challenges. Because the greatest risk in life, is the risk of never trying! Imagine what you could do here at Novartis! Commitment to Diversity & Inclusion: Novartis is committed to building an outstanding, inclusive work environment and diverse teams representative of the patients and communities we serve. Accessibility and Reasonable Accommodations: Individuals in need of a reasonable accommodation due to a medical condition or disability for any part of the application process, or to perform the essential functions of a position, please send an e-mail to tas.nacomms@novartis.com or call +1 (877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message. Join our Novartis Network : If this role is not suitable to your experience or career goals but you wish to stay connected to hear more about Novartis and our career opportunities, join the Novartis Network here: https://talentnetwork.novartis.com/network
Why Novartis: Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? https://www.novartis.com/about/strategy/people-and-culture
Join our Novartis Network: Not the right Novartis role for you? Sign up to our talent community to stay connected and learn about suitable career opportunities as soon as they come up: https://talentnetwork.novartis.com/network
Benefits and Rewards: Read our handbook to learn about all the ways we’ll help you thrive personally and professionally: https://www.novartis.com/careers/benefits-rewards
EEO Statement:
The Novartis Group of Companies are Equal Opportunity Employers who are focused on building and advancing a culture of inclusion that values and celebrates individual differences, uniqueness, backgrounds and perspectives. We do not discriminate in recruitment, hiring, training, promotion or other employment practices for reasons of race, color, religion, sex, national origin, age, sexual orientation, gender identity or expression, marital or veteran status, disability, or any other legally protected status. We are committed to fostering a diverse and inclusive workplace that reflects the world around us and connects us to the patients, customers and communities we serve.
Accessibility & Reasonable Accommodations
The Novartis Group of Companies are committed to working with and providing reasonable accommodation to individuals with disabilities. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the application process, or to perform the essential functions of a position, please send an e-mail to [email protected] or call +1(877)395-2339 and let us know the nature of your request and your contact information. Please include the job requisition number in your message.
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Location: Cambridge, MA #LI-Hybrid The AI & Computational Science (AICS) is the dedicated AI team at Novartis Biomedical Research innovating drug discovery. We aim to accelerate discoveries of transformative medicines for patients worldwide.AI & Computational Science (AICS) is seeking a highly motivated and experienced incoming "Principal AI researcher" in the domain of Image/Vision to push ...
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