Goal

The use of artificial intelligence (AI) is becoming increasingly important, especially in the field of medicine. This development presents prospective medical professionals with the challenge of not only acquiring sound specialist knowledge in the medical field, but also developing qualified application expertise and orientation knowledge for the appropriate use of AI applications. In order to ensure the efficient use of AI in medical practice, targeted training of people who work with these technologies is essential. Understanding how AI systems work and how they can be integrated into everyday medical practice is crucial in order to optimize the benefits of these technologies. This is why the BMBF-funded "TüKITZMed" project has set itself the goal of developing and establishing a cross-faculty, interprofessional curriculum with a focus on "AI in medicine" in the long term.

Format

Most of our learning material is available through an online learning platform. Students can browse a catalogue of available trainings and select courses of interest or follow pre-defined learning paths. The learning material is prepared in small learning nuggets of 5-10 minutes and presented in the form of videos, quizzes and exercises. We also plan to provide practical courses in the future.

Target group

Our course is aimed at, but not limited to, medical students, researchers working with medical data, and physicians. Our learning content is taught from the ground up and requires no special prior knowledge.

Certificate

By registering for the TüKITZMed course advertised at SIMED, medical students at the University of Tübingen can obtain a certificate. We offer this course once a term.

Most of the learning content is available through our online learning platform. If you would like access to the platform, please email tuekitzmedspam prevention@uni-tuebingen.de with the following information and we will create an account for you:

• Name
• Employee at the University of Tübingen or University Hospital Tübingen (yes/no)
• Student at the University of Tübingen (yes/no)

More information about the online learning platform can be found here.

We welcome contributions from professors, postdocs and doctoral students from the University of Tübingen as well as contributions from Tübingen-based companies.

Possible contributions are:

• Fundamental or advanced topics in mathematics or machine learning (ML)
• Applications of machine learning to medical data
• Ethical & legal aspects of working with medical data and/or applying machine learning techniques to it
• Practical sessions such as programming courses
• Hands-on courses on how to use ML-assisted diagnostics in daily clinical practice.

Please note that this is an incomplete list and we are open to discussing your ideas. Just send an email to tuekitzmedspam prevention@uni-tuebingen.de.