medical ai phd

The Lab for AI in Medicine at TU Munich develops algorithms and models to improve medicine for patients and healthcare professionals.

Our aim is to develop artificial intelligence (AI) and machine learning (ML) techniques for the analysis and interpretation of biomedical data. The group focuses on pursuing blue-sky research, including:

• AI for the early detection, prediction and diagnosis of diseases
• AI for personalised interventions and therapies
• AI for the identification of new biomarkers and targets for therapy
• Safe, robust and interpretable AI approaches
• Privacy-preserving AI approaches

We have particularly strong interest in the application of imaging and computing technology to improve the understanding brain development (in-utero and ex-utero), to improve the diagnosis and stratification of patients with dementia, stroke and traumatic brain injury as well as for the comprehensive diagnosis and management of patients with cardiovascular disease and cancer.

Daniel Rückert

Professor of artificial intelligence in healthcare and medicine.

Medical Image Computing, Data Science in Medicine, Artifical Intelligence in Medicine

Team Support

Deborah Carraro

Executive assistant.

Project Management and Administration, Team Management and Support, Communication and Relations

Sabine Franke

Adminstrative assistant, senior researchers.

Johannes C. Paetzold

Research scientist.

Graph representation learning, Computer vision, Biomedical image analysis

Computational oncology, Physics-based machine learning, Inverse problems

Dr. rer. nat. Simone Gehrer

Scientific manager.

Georgios Kaissis

Senior research scientist.

Privacy-preserving artificial intelligence, Medical image computing, Probabilistic methods

Martin Menten

Weakly and unsupervised deep learning, Generative modeling, Ophthalmologic imaging

Veronika Zimmer

Medical Image Computing, Ultrasound Image Analysis, Fetal Image Analysis

Researchers

Márton Szép

Phd student.

Natural Language Processing in Medicine, Generative Models, Multimodal AI

Florian A. Hölzl

Artifical Intelligence in Medicine, Privacy-preserving Deep Learning

Alexander Ziller

Artifical Intelligence in Medicine, Privacy-preserving Machine Learning

Anna Curto Vilalta

Foundation Models, Multi-Modal Deep Learning, AI in Medical Imaging

Alexander Berger

3D Medical Imaging, Weakly- and Self-supervised Transfer Learning, Domain Adaptation

Alexander Selivanov

AI in Medical Imaging, Multimodal Learning, Self-supervised Learning

Medical Imaging, Semantic Segmentation, Pancreatic Ductal Adenocarcinoma

Medical Imaging Computing

Ayhan Can Erdur

medical imaging, 3D computer vision, disease outcome prediction

Daniel Scholz

self-supervised learning, representation learning, 3d image segmentation of brain MRIs

Dmitrii Usynin

Artifical Intelligence in Medicine, Secure and Private Artificial Intelligence

Felix Meissen

Anomaly Detection, Transfer Learning, Generative Models, Bayesian Learning

Florent Dufour

AI in Medical Imaging, Trustworthy AI, Privacy Enhancing Technologies, Confidential Computing, Sovereign Cloud Computing

Friederike Jungmann

human-in-the-loop-machine learning, interpretable artificial intelligence, medical image computing

Artificial Intelligence in Medicine, Fairness and Bias in Healthcare

Hendrik Möller

MRI Segmentation, MRI Vertebrae Detection and Labeling, Transitional Vertebrae

Jiazhen Pan

Medical Imaging Computing, Semantic Segmentation, Medical Image Reconstruction

Johannes Kaiser

AI in Medical Imaging, Privacy-preserving Machine Learning, Trustworthy Machine Learning

Jonas Kuntzer

Mechanistic interpretability, Differential privacy

Jonas Weidner

Personalized brain tumor modeling, Physics-informed neural networks, Diffusion tensor imaging, Topological data analysis

Julian McGinnis

Medical Imaging, Implicit Neural Representations, Multiple Sclerosis Research

Kristian Schwethelm

AI in Medical Imaging, Privacy-preserving Machine Learning, Differential Geometry

AI in Biomedical Imaging, Graphs in Medical AI, Interpretable AI

Leonhard Feiner

Machine Learning and Deep Learning, Medical Image Computing, Data Science

Linus Kreitner

Weakly- and Selfsupervised Machine Learning, Network Dissection and Explainability, Causal Inference

Maik Dannecker

Medical Imaging, Deep Learning, Biomarker Discovery, Demystifying The Human Brain

Generative Models and Latent Spaces, Unsupervised Learning, Graph Neural Networks

Maulik Cevalī

Privacy-preserving ML, Trustworthy ML, Applied AI in Medicine

Moritz Knolle

Differential Privacy, Fair & Trustworthy ML, Memorisation in Neural Networks

Niklas Bubeck

Generative AI in Medical Imaging, Medical Image Reconstruction, Multi-Modal Foundation Models

Nil Stolt-Ansó

Medical Image Segmentation, Image Registration, Geometric Deep Learning

Medical Imaging Computing, Multi-modal Deep Learning, Genetics

Philip Müller

Multi-Modal Learning, Natural Language Processing, Geometric Deep Learning

Reihaneh Torkzadehmahani

Phd student.

Privacy-preserving Machine Learning, Meta and Transfer Learning, Generative Models

Reza Nasirigerdeh

Privacy-preserving machine learning, Distributed systems, Medical imaging

Ricardo Smits Serena

Medical Wearable Technology, Time Series Classification, Gait Analysis

Robert Graf

Computer Vision for Spine Processing, Image2Image, Denoising Diffusion, Large Epidemiological Studies

Sarah Lockfisch

AI in Medical Imaging, Interpretability in Deep Learning, Uncertainty Quantification

Shuting Liu

Multi-modality Image Analysis, Domain Transfer

Sophie Starck

Machine Learning, Geometric Deep Learning, Medical Image Computing

Tamara Müller

Artificial Intelligence in Medicine, Geometric Deep Learning, Computational Neuroscience

Vasiliki Sideri-Lampretsa

Artifical Intelligence in Medicine, Machine Learning and Deep Learning, Medical Imaging

Wenke Karbole

Generative AI, Temporal Representation Modeling, Ophthalmologic Imaging

Wenqi Huang

Image Reconstruction, Multi-Task Deep Learning

Yundi Zhang

Deep Learning, Medical Image Computing, MRI

Özgün Turgut

Signal processing, Self-supervised learning, Multimodal AI

Collaborators

Florian Hinterwimmer

Affiliated researcher.

Multimodal machine learning, Applied AI in musculoskeletal medicine, Data engineering and medical informatics

Franz Rieger

Affliated researcher.

ML for connectomics, Self-supervised segmentation, ML for code

Henrik von Kleist

Interpretable AI, Uncertainty quantification in ML, Causal inference

Kerstin Hammernik

Inverse Problems, Machine Learning, MRI, Medical Image Computing

New Module IN2409: Inverse Problems in Medical Imaging

Recent Publications

The developing human connectome project (dhcp) automated resting-state functional processing framework for newborn infants, model-based and data-driven strategies in medical image computing.

Secure, privacy-preserving and federated machine learning in medical imaging

A population-based phenome-wide association study of cardiac and aortic structure and function

Genetic and functional insights into the fractal structure of the heart

A Privacy-Preserving Framework for Multi-Entry Medical Datasets

In this master’s thesis, we aim to explore the potential of leveraging multiple data entries per contributor across various medical datasets (ex. Chexpert) while maintaining differential privacy guarantees at the per-contributor level.

MSc Thesis: Large Language Models in Medicine

Description: Large Language Models (LLMs) have shown exceptional capabilities in understanding and generating human-like text. In the medical field, these models hold the potential to revolutionize patient care, medical research, and healthcare administration.

MSc Thesis: Leveraging Differential Privacy to Learn General and Robust Deep Learning Models

Description Deep learning aims at learning general representations of data allowing for downstream tasks such as classification, regression or generation of new data. In practice, however, there are no formal guarantees to what a model learns, resulting in unwanted memorisation of input data and leaking of private information.

MSc Thesis: Outperforming CNNs and Transformers on Medical Imaging Tasks with Equivariant Networks

Description Equivariant convolutions are a novel approach that incorporate additional geometric properties of the input domain during the convolution process (i.e. symmetry properties such as rotations and reflections) [1]. This additional inductive bias allows the model to learn more robust and general features from less data, rendering them highly promising for application in the medical domain.

MSc Thesis: Privacy-Preserving Synthetic Time Series Data of Electronic Health Records

Description Anonymizing data means removing or replacing any identifying information from a dataset, such as names or addresses. The aim of anonymization is to protect the privacy of individuals whose data is being collected and processed.

Master-Seminar: Multi-modal AI for Medicine (IN2107)

This year’s seminar will look at aspects of multi-modal machine learning in medicine and healthcare, focusing on: Vision language models (VLMs) for medical and healthcare applications Generic multi-modal AI models utilising imaging data, clinical reports, lab test results, electronic health records, and genomics Foundation models for multi-modal medicine Objectives: At the end of the module students should have:

Practical Course: Applied Deep Learning in Medicine

In this course students are given the chance to apply their abilities and knowledge in deep learning to real-world medical data. Students will be assigned a medical dataset and in close consultation with medical doctors create a project plan.

Master Thesis: Deep Learning for Bone Tumor Detection and Segmentation: 2D vs 3D

Abstract: The detection and segmentation of bone tumors using magnetic resonance imaging (MRI) have crucial implications for clinical diagnosis and treatment planning. With the advent of deep learning techniques, there’s a growing interest in leveraging these methods to analyze MRI bone tumor images.

MSc Thesis: Diffusion-based Topology-preserving Medical Image Segmentation

This project can be hosted in Munich and/or Zurich @Biomedical Image Analysis & Machine Learning Group, University of Zurich. Background: Topology is vital in medical image segmentation, emphasizing anatomically correct structures & removing incorrect ones.

IDP/Thesis: Physics-based deep learning for hyperspectral brain surgery imaging

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object’s reflectance allows fingerprinting its physical, biochemical, and physiological properties.

IDP: iOS app for wearable health data management

​ Description In an era where technology has seamlessly integrated with our day-to-day lives, health and fitness tracking has seen a revolutionary change. Gone are the days when we passively absorbed health information.

MSc Thesis: Contrastive Learning and Generative Models for Cross-Domain Transfer Learning

In this Master thesis we aim to approach the cross-domain transfer learning problem with two powerful methods that help us to bridge the domain gap between source and target domain: contrastive learning [1] and generative models.

We are recruiting team members who would like to join us for a MSc, BSc or guided research/interdisciplinary project on an ongoing basis! Please look under Teaching to find out which projects we are currently offering. If you’d like to join us for one of these projects, please get in touch by contacting the appropriate staff member via e-mail and attach a motivation letter, transcript of academic records and CV.

Current vacancies

Currently no positions are available.

Internships

Unfortunately we cannot host any external students for internships.

• [email protected]
• +49 89 4140-8851
• TranslaTUM, Einsteinstraße 25 81675 München

The AIMI Center

The Stanford Center for Artificial Intelligence in Medicine and Imaging (AIMI) was established in 2018 to responsibly innovate and implement advanced AI methods and applications to enhance health for all.

Director's Welcome

Back in 2017, I tweeted “radiologists who use AI will replace radiologists who don’t.”  The tweet has taken on a life of its own, perhaps because it has a double meaning.

AIMI Impact Report

This report highlights the progress of our research, education, and policy programs during our first five years (2018-2023).

AIMI Symposium Recordings

Recordings for the 5th annual AIMI Symposium (all sessions) are available on the AIMI YouTube channel.

AIMI Dataset Index

AIMI has launched a community-driven resource of health AI datasets for machine learning in healthcare as part of our vision of catalyze sharing well curated, de-identified clinical data sets

AIMI NextGen Tech Talk: Nishith Khandwala

Join us online on August 26th, 5pm PT for Episode 5 of our NextGen Tech Talk Series . This webinar series is tailored towards high school students who are eager to explore the field of AI and health. 

AIMI Datasets for Research & Commercial Use

The AIMI Center is helping to catalyze outstanding open science by publicly releasing 20+ AI-ready clinical data sets (many with code and AI models) for research and commercial use.

Upcoming Events

Ibiis-aimi seminar: sophie ostmeier, md & jeong hoon lee, phd, the ai touch: harnessing ai to enrich patient care - yaa a kumah-crystal, md, mph, ibiis-aimi seminar: ipek oguz, phd.

The Centre for Doctoral Training in AI for Medical Dia gnosis and Care is training over 50 graduates to beco me highly effective researchers, team-workers, communicators and innovators in AI within the health  domain, publishing at the highest international level and equipped to unlock the immense potential of AI in everyday medical practice.

Aims of the Centre

• A sustainable and internationally outstanding Centre for research training in the application of AI to medical diagnosis and care;
• A research training environment with exemplary adherence to principals of equality, diversity and inclusion, targeting a 50-50 ratio of female to male graduates drawn from a broad range of health and STEM backgrounds;
• National leadership in the development of software systems for ensuring security and privacy in the use of health-related data and compute-intensive algorithms in the cloud;
• Graduates who embody a culture of innovation and world class leadership, ensuring the UK remains at the forefront in health research, provision and commercial innovation;
• Seeding of larger research collaborations with industry, the public sector and international university partners of the Centre;
• Collaborative exploitation of new research ideas arising from the Centre in conjunction with industry partners, and technology transfer agencies.

Centre for Doctoral Training in AI-enabled Healthcare

Partnerships

• How to apply and funding
• News & Success Stories
• Publications

UKRI UCL Centre for Doctoral Training in AI-enabled Healthcare Systems

The UKRI  Centre for Doctoral Training (CDT) in AI-enabled Healthcare Systems combines UCL’s excellence both in AI and computational science, and in biomedical research. We offer a unique programme consisting of a 1 year MRes followed by a 3 year PhD  embedded within an NHS setting. Applications now open.

How to apply

UCL is home to cutting-edge research not only in artificial intelligence in healthcare, but also the whole spectrum of AI innovation from education, law and social science to engineering, computer science and biology. By joining our CDT, you will have the opportunity to work with inspirational world-leading academics to transform healthcare systems using AI for patient benefit. Professor Geraint Rees, CDT Director and UCL Pro-Vice-Provost (AI)

Keep up to date with all matters AI related. On the form please select: PhD as the level of study and Health Informatics as the subject of interest.

Register your interest

Antonia Coote, CDT Manager

[email protected]

Follow @cdt_ai_health

• Whiting School of Engineering
• Johns Hopkins School of Medicine

• Johns Hopkins Biomedical Engineering
• Master’s Programs
• Master’s Program
• Master’s Focus Areas & Courses

AI in Medicine for Medical Trainees

Ai in medicine focus area curriculum requirements.

Increasingly, the decisions physicians make about how best to treat their patients will be informed by the results of computational analyses of patient data. This increasing reliance on methods of artificial intelligence to guide patient care will not only transform medicine, but will also transform the ways in which physicians are trained. Future physicians will need to understand core principles of data science and be able to apply them to critically evaluate the emerging literature on AI in medicine and to do research in this emerging field.

Over the course of two semesters, the AI in Medicine focus area provides medical students, residents, and clinical fellows with the advanced training needed to think critically about topics in data science, and to pursue careers in research or development. Training in this focus area can be individually tailored to meet the needs of medical trainees from diverse educational backgrounds. Basic course requirements in this focus area allow for maximum flexibility in curriculum design.

Limited scholarship funds may be available by application to AI in Medicine students who are currently affiliated with the Johns Hopkins Medical Institutions.

• Biomedical Data Science (EN.580.475)
• Biomedical Data Science Lab (EN.580.477)
• Biomedical Data Design I and II (EN.580.697 and EN.580.638)
• Precision Care Medicine I and II (EN.580.680/681)
• Artificial Intelligence System Design & Development (EN.601.686)
• Clinical Data Analysis with Python (ME.600.720)
• Computational Molecular Medicine (EN.553.650)
• Computer Integrated Surgery I (EN.601.655 (01))
• Computer Vision (EN.601.661)
• Data Mining (EN.553.636)
• Deep Learning (EN.520.638)
• Deep Learning: Data to Models (EN. 601.676)
• Deep Learning for Automated Discourse (EN.601.767)
• Deep Learning in Discrete Optimization (EN.553.667)
• Foundations of Computational Biology and Bioinformatics (EN.580.688)
• Introduction to Computational Medicine (EN580.631)
• Introduction to Probability (EN.553.620)
• Introduction to Statistics (EN.553.630)
• Learning, Estimation and Control (EN.580.691)
• Machine Learning (EN.601.675)
• Machine Learning I & II (EN.553.740/1)
• Machine Learning: Deep Learning (EN.601.682)
• Machine Learning for Signal Processing (EN.520.612)
• Mathematics of Deep Learning (EN.580.745)
• Sparse Representations in Computer Vision & Machine Learning (EN.580.709)
• Vision as Bayesian Inference (EN. 601. 783)
• Data Science for Public Health I (PH.140.628.71)
• Data Science for Public Health II (PH.140.629.71)
• *Instructor and advisor approvals required with the submission of an interdivisional registration form through SEAM

Read the Johns Hopkins University privacy statement here .

Lafata Lab Overview

The  Lafata Laboratory  focuses on the theory, development and application of computational oncology. The lab interrogates disease at different length-scales of its biological organization via high-performance computing, multiscale modeling, advanced imaging technology and the applied analysis of stochastic partial differential equations. Current research interests include tumor topology, cellular dynamics, tumor immune microenvironment, drivers of radiation resistance and immune dysregulation, molecular insight into tissue heterogeneity and biologically-guided adaptative treatment strategies.

Kyle Lafata, PhD, is the Thaddeus V. Samulski Associate Professor at Duke University with faculty appointments in Radiation Oncology, Radiology, Pathology, Medical Physics and Electrical & Computer Engineering. He joined the faculty at Duke in 2020 following postdoctoral training at the US Department of Veterans Affairs. His dissertation work focused on the applied analysis of stochastic partial differential equations and high-dimensional image phenotyping, where he developed physics-based computational methods and soft-computing paradigms to interrogate images. These included stochastic modeling, self-organization and quantum machine learning (i.e., an emerging branch of research that explores the methodological and structural similarities between quantum systems and learning systems). Dr. Lafata has worked in various areas of computational medicine and biology, resulting in over 55 academic papers, 20 invited talks and more than 60 national conference presentations.

Visit the Lafata Lab

New R01 Awarded to Medical Physics Faculty Lo, Lafata to Study AI Tools for CTs

Kyle Lafata, PhD,  is a co-investigator on a newly awarded $2.3 million, four-year R01 grant by the NIH/NCI through Radiology titled "Computer-Aided Triage of Body CT Scans with Deep Learning." The PI is Radiology and Medical Physics faculty member Joseph Lo, PhD ; the multidisciplinary team also includes Duke faculty members Cynthia Rudin, PhD, and Sheng Luo, PhD. "This project will develop AI tools for chest, abdomen and pelvis CTs, simultaneously triaging 17 different organ systems for over 100 diseases," said Dr.

Kyle J. Lafata, PhD, Named Thaddeus V. Samulski Assistant Professor of Radiation Oncology

Kyle J. Lafata, PhD, has been named the Thaddeus V. Samulski Assistant Professor of Radiation Oncology, effective April 1, 2023.

Dr. Lafata works within the Departments of Radiation Oncology, Radiology, and Electrical and Computer Engineering at Duke University; he also serves as a faculty member for the Duke Medical Physics Graduate Program.

Lafata Receives New Grant for Racial Disparities Research

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How AI innovation is reinventing medical education

The AI revolution is transforming medical education at an unprecedented pace, offering game-changing opportunities to personalize learning experiences, support medical educators in their daily tasks, and optimize education management in medical schools and teaching hospitals.

Recent research demonstrates the immense potential of AI to boost productivity in medical knowledge work, with studies showing that medical professionals using advanced AI models completed more tasks, worked faster, and produced higher-quality outputs compared to those without AI assistance.

For medical students, AI-powered personalized learning platforms are adapting to individual needs and providing real-time feedback. AI-powered simulations and virtual patients offer realistic training environments for practicing clinical skills and decision-making. Natural language processing tools are revolutionizing how students review and synthesize medical literature. However, the controversial use of generative AI tools like ChatGPT for assignments highlights the need for educators to adapt assessment strategies, foster AI literacy and guide students in the responsible use of these technologies.

For medical educators, AI is supporting curriculum design, providing automated assessment and feedback, enhancing lecture preparation and offering virtual teaching assistance. These tools are helping to reduce administrative burdens and allowing educators to focus more on mentoring and hands-on teaching.

In education administration, AI-powered predictive analytics are helping identify at-risk students and optimize resource allocation. AI-driven admissions processes are enhancing efficiency and potentially reducing bias. Research collaboration tools are identifying funding opportunities and facilitating interdisciplinary work.

However, harnessing AI's potential in medical education requires addressing key challenges. First, ensuring equitable access to AI tools and addressing the digital divide is crucial. Second, developing ethical governance frameworks for AI use in medical training is essential. Third, balancing AI integration with the development of human expertise and empathy in patient care is a critical consideration.

It is essential to emphasize that AI should not be seen as a replacement for human expertise in medical education but rather as a way to enhance and scale the impact of human judgment and skills. The role of medical educators remains critical, and AI tools should be viewed as powerful assistants that can help personalize learning experiences, provide targeted support, and make data-driven decisions.

In conclusion, the AI revolution in medical education presents both immense opportunities and complex challenges. By understanding the current landscape, anticipating future trends, and proactively addressing challenges, medical schools and teaching institutions can harness the transformative power of AI to create inclusive, innovative, and effective learning experiences for future healthcare professionals. This will require ongoing collaboration among policymakers, educators, researchers, healthcare providers, and technology developers to ensure that AI is developed and deployed in a way that benefits all learners while mitigating potential risks and unintended consequences in the sensitive field of healthcare education.

Dr. Rubin Pillay is the Marnix E. Heersink Professor of Biomedical Innovation and Assistant Dean at UAB's School of Medicine. He also serves as Executive Director of the Heersink Institute for Biomedical Innovation and Chief Innovation Officer for UAB Health System. Additionally, Dr. Pillay is the Editor-in-Chief of Innovation and Entrepreneurship in Health.

If you're interested in additional insights on this topic from Dr. Pillay, please email Mackenzie Bean at [email protected]

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Artificial Intelligence in Radiation Therapy

Published by Institute of Physics Publishing : December 2022: Series: IPEM-IOP Series in Physics and Engineering in Medicine and Biology

Iori Sumida

Artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. The book provides applications of artificial intelligence (AI) in radiation therapy according to the clinical radiotherapy workflow. An introductory section explains the necessity of AI regarding accuracy and efficiency in clinical settings followed by a basic learning method and introduction of potential applications in radiotherapy. Some chapters also include typical source codes which the reader may use in their original neural network. 

This book would be an excellent text for more experienced practitioners and researchers and members of medical physics communities, such as AAPM, ASTRO, and ESTRO. Students and graduate students who are focusing on medical physics would also benefit from this text.

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