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030108 KU Artificial Intelligence and medical law (2023S)
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 Tu 07.02.2023 00:01 to Tu 21.02.2023 23:59
- Deregistration possible until Tu 14.03.2023 23:59
Details
max. 40 participants
Language: German
Lecturers
Classes (iCal) - next class is marked with N
- Tuesday 14.03. 17:00 - 19:00 Hörsaal U22 Schottenbastei 10-16, Juridicum, KG2 (Kickoff Class)
- Monday 27.03. 17:00 - 20:00 Hörsaal U14 Schottenbastei 10-16, Juridicum, KG1
- Monday 08.05. 17:00 - 20:00 Seminarraum SEM62 Schottenbastei 10-16, Juridicum 6.OG
- Tuesday 09.05. 17:00 - 20:00 Seminarraum SEM52 Schottenbastei 10-16, Juridicum 5.OG
- Wednesday 10.05. 17:00 - 20:00 Hörsaal U16 Schottenbastei 10-16, Juridicum, KG1
- Thursday 11.05. 17:00 - 20:00 Hörsaal U16 Schottenbastei 10-16, Juridicum, KG1
- Friday 12.05. 14:00 - 20:00 Hörsaal U15 Schottenbastei 10-16, Juridicum, KG1
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.
Active participation in the discussion of the presentations.
Preparation of a thesis paper on the presentation.
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: Mo 27.03.2023 11:48