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1st Semester – Technical Foundations, Clinical Context & Regulatory Frameworks
In the first semester, you will acquire the methodological and technical foundations for applying AI in healthcare.
Focus Areas:
- Data Science & Machine Learning fundamentals
- Programming and data processing
- Building robust data pipelines and model evaluation
- Introduction to clinical data, standards, and system integration
- Legal frameworks (EU AI Act, MDR, etc.)
- Ethics and societal impact of AI
- Agile project management in healthcare
Goal: Understand how reproducible and safe AI workflows are developed from clinical challenges.
2nd Semester – Applications, Use Cases & First Projects
The second semester focuses on practical application. You will work on concrete healthcare-related challenges.
Focus Areas:
- Applying AI methods to real clinical use cases
- Data preparation, model training, and validation
- Developing initial AI projects in teams
- Explainable AI and model transparency
- Quality assurance and human oversight
- Philosophical and societal perspectives on AI
- Exchange with experts through lecture series
Goal: Bridge the gap between theory and responsible pilot implementation.
3rd Semester – Advanced Topics & Clinical Integration
In the third semester, you will deepen your expertise in specific application areas and learn how to integrate AI into complex healthcare systems.
Focus Areas:
- Medical Imaging & AI (e.g. radiology, diagnostics)
- Clinical Decision Support Systems
- Practical implementation of regulatory requirements
- European Health Data Space and data-driven healthcare
- Communication, transformation & change management
- Elective modules for individual specialisation
- Development of a research design for the Master’s thesis
Goal: Understand, evaluate, and integrate AI solutions into real clinical processes.
4th Semester – Master Project & Implementation
In the final semester, you will complete a comprehensive practical or research-based project.
Focus Areas:
- Independent Master project in cooperation with clinics, research institutions, or companies
- Applying all acquired competencies to real-world challenges
- Working with real healthcare data
- Scientific thesis and final presentation
- Exchange on current research topics (Reading Groups, Expert Talks)
Goal: Develop and implement an AI solution with direct practical relevance.