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Artificial Intelligence in Medicine

Module IN2403

This Module is offered by TUM Department of Informatics.

This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective sections.

Basic Information

IN2403 is a semester module in English language at Master’s level which is offered every semester.

This Module is included in the following catalogues within the study programs in physics.

  • Focus Area Imaging in M.Sc. Biomedical Engineering and Medical Physics
Total workloadContact hoursCredits (ECTS)
150 h 60 h 5 CP

Content, Learning Outcome and Preconditions

Content

• Introduction: Clinical motivation, clinical data, clinical workflows
• ML for medical imaging
• Data curation for medical applications
• Domain shift in medical applications: Adversarial learning and Transfer learning
• Self-supervised learning and unsupervised learning
• Learning from sparse and noisy data
• ML for unstructured and multi-modal clinical data
• NLP for clinical data
• Bayesian approaches to deep learning and uncertainty
• Interpretability and explainability
• Federated learning, privacy-preserving ML and ethics
• ML for time-to-event modeling, survival models
• ML for differential diagnosis and stratification
• Clinical applications in pathology/radiology/omics

Learning Outcome

At the end of the module students should be able to recall the important topics in the area of artificial intelligence in medicine, understand the relations between the topics, apply their knowledge to own deep learning projects, analyse and evaluate social and ethical implications and develop own strategies to apply the learned concepts to their own work.

Preconditions

Introduction to Deep Learning (IN2346)
Computer Aided Medical Procedures I (IN2021)

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

TypeSWSTitleLecturer(s)DatesLinks
VO 4 Artificial Intelligence in Medicine (IN2403) Rückert, D. Schnabel, J. Tue, 16:15–17:45, virtuell
Thu, 16:15–17:45

Learning and Teaching Methods

Lecture, tutorial, problems for individual study. Guest lectures will be held by experts from local hospitals and med-tech companies to ensure that the covered topics are relevant for clinical practice. Assignments are provided on a weekly basis via the teaching portal and will be aligned to the lectures and tutorials in terms of content. They are discussed in the next tutorial class, and a solution is presented. Work on the assignments and participation in the tutorial class are voluntary. They serve as a means for students to deepen and test their acquired knowledge – as a self-monitoring aid to prepare for the written exam.

Media

Slide show, blackboard

Literature

Recommended:
1. I. Goodfellow, Y. Bengio and A. Courville. Deep Learning. MIT Press, 2016. Available at http://www.deeplearningbook.org
2. E. J. Topol. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 25, 44–56, 2019. https://doi.org/10.1038/s41591-018-0300-7
3. A. Esteva, K. Chou, S. Yeung, et al. Deep learning-enabled medical computer vision. npj Digit. Med. 4, 5, 2021. https://doi.org/10.1038/s41746-020-00376-2
4. B. Norgeot, G. Quer, B.K. Beaulieu-Jones et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat Med 26, 1320–1324 (2020). https://doi.org/10.1038/s41591-020-1041-y
5. V. Sounderajah, H. Ashrafian, R. Aggarwal et al. Developing specific reporting guidelines for diagnostic accuracy studies assessing AI interventions: The STARD-AI Steering Group. Nat Med 26, 807–808 (2020). https://doi.org/10.1038/s41591-020-0941-1

Optional
6. E. Topol. Deep Medicine - How Artificial Intelligence Can Make Healthcare Human Again. 2019.

Module Exam

Description of exams and course work

The exam takes the form of a written test. The duration is 90 minutes, and no material is allowed (closed book). Questions allow to assess whether the student is able to understand fundamentals, differences and application areas of Artificial Intelligence in Medicine as well as methods for computer-aided diagnosis and decision making. Using practical case studies will assess whether the student is able to select an appropriate artificial intelligence or machine learning approach for a given application. All problems and questions demand the students to phrase their individual responses.

Exam Repetition

There is a possibility to take the exam in the following semester.

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