Artificial Intelligence in Medicine
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.
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 workload||Contact hours||Credits (ECTS)|
|150 h||60 h||5 CP|
Content, Learning Outcome and Preconditions
• 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
Computer Aided Medical Procedures I (IN2021)
Courses, Learning and Teaching Methods and Literature
Courses and Schedule
|VO||4||Artificial Intelligence in Medicine (IN2403)||Rückert, D. Schnabel, J.||
Tue, 16:15–17:45, virtuell
Learning and Teaching Methods
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
6. E. Topol. Deep Medicine - How Artificial Intelligence Can Make Healthcare Human Again. 2019.
Description of exams and course work
There is a possibility to take the exam in the following semester.