Short (two sessions) introduction to the ethical issues of Medical AI applications. We identify the ethical implications of AI applications in Medicine, focus on data collection and use, the risks of artificial intelligence technologies, and bias and discrimination, and address the impact of automated decision-making in Medical AI Applications. The lectures are in English and are held online.

This course is offered to medical students interested in WissPro 1 and 2, involving literature research. It includes an introductory lecture on literature search strategies and best practices, as well as presentations on the topics offered by different members of our institute. The work is organised according to a schedule with several checkpoints and concludes with on-site presentations by the participating students.

Artificial intelligence is already fundamentally changing medicine, but how do the underlying methods work, and what opportunities and challenges do they present? In this series of seminars, each session will cover a new, practical topic, including the basics of some AI methods, ethical challenges and possible solutions. The lectures, depending on the speaker, could be in German or English, are thematically linked but self-contained

Technologische Fortschritte wie KI und Big Data werden das Gesundheitswesen positiv verändern. Bei diesen intelligenten Gesundheits- und Medizintechnologien kommen in der Regel bahnbrechende und disruptive Innovationen zum Einsatz. Ihre Einführung wird jedoch häufig durch mangelndes Bewusstsein und mangelnde Bereitschaft anderer Akteure in den jeweiligen Märkten und Branchen behindert. In diesem Kurs werden wir Beispiele spezifischer Ökosysteme untersuchen und mit Hilfe von Open-Innovation-Methoden und -Werkzeugen deren Grenzen aufzeigen. Der Kurs basiert auf den Ergebnissen und laufenden Forschungsarbeiten des Horizon Europe SHIFT-HUB-Projekts, das darauf abzielt, einen patientengesteuerten Ansatz für die Entwicklung und Einführung intelligenter Gesundheitslösungen zu entwickeln.

The course aims to familiarise students with the basic concepts of the domain and to highlight the challenges posed by the explosion of data-driven applications using Artificial Intelligence (AI) in recent years. Topics to be covered include an introduction to data-driven AI and Machine Learning, artificial agents, privacy and consent, bias and discrimination. The course is entirely online, held in English, and assessed through presentations based on scientific publications selected by the students in collaboration with the tutor.

Lecture introduces the principles and practice of Open Science and the FAIR (Findable, Accessible, Interoperable, Reusable) principles in a clear and accessible way for researchers across disciplines. It explores why transparency, collaboration, and responsible data sharing are becoming central to research, particularly in light of funder requirements under programmes such as Horizon Europe. Participants will gain an overview of key concepts, policy context, and practical steps to integrate Open Science and FAIR principles into their everyday research workflows.