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Artificial Intelligence in Medicine II

Module IN2408

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

IN2408 is a semester module in English language at Master’s level which is offered in summer 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


Introduction and examples of advanced prediction and classification problems in medicine; ML for prognostic and diagnostic tasks; risk scores, time-to-event modeling, survival models, differential diagnosis & population stratification, geometric deep learning: point clouds & meshes, mesh-based segmentation, shape analysis, trustworthy AI in medicine: bias and fairness, generalizability, AI for affordable healthcare, clinical deployment and evaluation, data harmonization, causal inference, transformers, reinforcement learning in medicine, ML for neuro: structural neuroimaging, functional neuroimaging, diffusion imaging, ML for CVD: EEG analysis

Learning Outcome

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


IN2403 Artificial Intelligence in Medicine

Courses, Learning and Teaching Methods and Literature

Courses and Schedule

VI 4 Artificial Intelligence in Medicine II (IN2408) Rückert, D. Schnabel, J. Wachinger, C. Wed, 16:00–18:00, GALILEO Taurus
Thu, 14:00–16:00, GALILEO Taurus
and singular or moved dates

Learning and Teaching Methods

Interactive Lecture, theoretical and practical Exercises


PowerPoint, Whiteboard


Rajpurkar, P., Chen, E., Banerjee, O. et al. AI in health and medicine. Nat Med 28, 31–38 (2022).

Y. Chen et al., "AI-Based Reconstruction for Fast MRI—A Systematic Review and Meta-Analysis," in Proceedings of the IEEE, vol. 110, no. 2, pp. 224-245, Feb. 2022, doi: 10.1109/JPROC.2022.3141367.

Roberts, M., Driggs, D., Thorpe, M. et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell 3, 199–217 (2021).

Module Exam

Description of exams and course work

Lecture: Written examinations (90 min) to see if the students have acquired deep understanding of the provided mathematical tools
Practical: Five assignment sheets with theoretical exercises and practical programming tasks (20% of the total grade)

Exam Repetition

The exam may be repeated at the end of the semester.

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