030201 KU Artificial Intelligence and medical law (2024S)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 12.02.2024 00:01 to Mo 26.02.2024 23:59
- Deregistration possible until Th 14.03.2024 23:59
Details
max. 30 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
Monday
11.03.
11:00 - 13:00
Seminarraum SEM33 Schottenbastei 10-16, Juridicum, 3.OG
(Kickoff Class)
Monday
08.04.
11:00 - 14:00
Seminarraum SEM34 Schottenbastei 10-16, Juridicum, 3.OG
Monday
15.04.
11:00 - 14:00
Seminarraum SEM42 Schottenbastei 10-16, Juridicum, 4.OG
Monday
13.05.
11:00 - 14:00
Seminarraum SEM33 Schottenbastei 10-16, Juridicum, 3.OG
Tuesday
14.05.
11:00 - 14:00
Seminarraum SEM63 Schottenbastei 10-16, Juridicum 6.OG
Tuesday
21.05.
11:00 - 14:00
Seminarraum SEM42 Schottenbastei 10-16, Juridicum, 4.OG
Wednesday
22.05.
11:00 - 16:00
Seminarraum SEM34 Schottenbastei 10-16, Juridicum, 3.OG
Information
Aims, contents and method of the course
Overview of the legal issues (e.g. data protection law, fundamental rights, professional law, liability law) arising from the use of artificial intelligence in medicine (e.g. diagnosis, drug research, chatbots). After an introduction to the technical and legal basics by the lecturers, the students give presentations on selected issues and prepare a thesis paper. The problems will be discussed together.
Assessment and permitted materials
Oral presentation on a selected topic.
Preparation of a thesis paper on the presentation. The use of large language models for the preparation of this paper is permitted but must be acknowledged in the paper. Students remain solely responsible for the contents of their paper.
Active participation in the discussion of the presentations.
Preparation of a thesis paper on the presentation. The use of large language models for the preparation of this paper is permitted but must be acknowledged in the paper. Students remain solely responsible for the contents of their paper.
Active participation in the discussion of the presentations.
Minimum requirements and assessment criteria
50% presentation, 30% thesis paper, 20% participation. For a positive assessment, the presentation must be held and positively evaluated.
Examination topics
Delivery of an oral presentation + preparation of a thesis paper + active participation in the discussion sessions.
Reading list
General: Topol, Deep Medicine (2019).Legal introduction: Paar/Stöger, Medizinische KI - Die rechtlichen "Brennpunkte", in Fritz/Tomaschek (Hrsg), Konnektivität (2021) 85; Schneeberger/Stöger/Holzinger, The European Legal Framework for Medical AI, in Holzinger ea (Hrsg), Machine Learning and Knowledge Extraction (2020) 209; Schönberger, Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications, International Journal of Law and Information Technology 2019, 171; Stöger/Schneeberger/Holzinger, Medical artificial intelligence: the European legal perspective, Communications of the ACM 11/2021, 34.Technical introduction: Alpaydin, Machine Learning. The New AI (2. Edition 2021); Burgstaller/Hermann/Lampesberger, Künstliche Intelligenz. Technisches und rechtliches Grundwissen (2019); Domingos, The Master Algorithm (2015); Kelleher, Deep Learning (2019); Lehr/Ohm, Playing with the Data:
What Legal Scholars Should Learn About Machine Learning, UCDL Rev 2017, 653, https://lawreview.law.ucdavis.edu/issues/51/2/Symposium/51-2_Lehr_Ohm.pdf
What Legal Scholars Should Learn About Machine Learning, UCDL Rev 2017, 653, https://lawreview.law.ucdavis.edu/issues/51/2/Symposium/51-2_Lehr_Ohm.pdf
Association in the course directory
Last modified: Tu 14.05.2024 13:05