Zeyd Boukhers (Dr.)

[Kurs wird nur in Englisch angeboten] Large Language Models (LLMs) and Knowledge Graphs are rapidly transforming clinical practice as powerful tools for medical decision support, documentation, and research. This course teaches medical students to understand and implement these technologies through hands-on coding with LLM endpoints and existing medical knowledge graphs. Students learn fundamental concepts of how LLMs process medical text and how knowledge graphs structure clinical information, then apply this knowledge by writing code to build Knowledge Graph-Retrieval Augmented Generation (KG-RAG) systems that combine both approaches. Students work with established medical knowledge bases like UMLS, SNOMED CT, and DrugBank, integrating them with LLM APIs to create robust clinical tools. Through coding exercises, participants build systems that leverage structured medical knowledge to improve LLM accuracy and reduce hallucinations in clinical contexts. Students evaluate their implementations for medical reliability, learning to identify limitations and bias in AI-generated clinical content. This practical approach prepares future physicians to critically assess, implement, and optimize AI tools in their clinical practice, making it essential training for modern evidence-based medicine.