PhD Candidate · FH Dortmund & IKIM WisPerMed
Hendrik Damm
PhD candidate in the DFG Research Training Group WisPerMed, researching how NLP and large language models can support personalized medicine, clinical text processing, and medical information retrieval.
Research Areas
What I Work On
Healthcare NLP
Applying large language models to clinical text — from discharge summary generation and lay summarization to patient-oriented language processing.
Medical Information Retrieval
Co-organizing ImageCLEF medical tasks and researching multilingual information retrieval across clinical languages in Europe.
Knowledge Graphs & Text Mining
Automated construction of biomedical knowledge graphs from literature and text mining for evidence-based medicine.
Personalized Medicine
Part of the DFG Research Training Group WisPerMed, working on knowledge- and data-based personalisation of medicine at the point of care.
Selected Work
Featured Publications
Overview of ImageCLEF 2025: Multimedia Retrieval in Medical, Social Media and Content Recommendation Applications
CLEF 2026 (Springer)
WisPerMed at ArchEHR-QA 2025: A Modular, Relevance-First Approach for Grounded Question Answering on Electronic Health Records
BioNLP Workshop @ ACL 2025
Overview of ImageCLEFmedical 2025 — Medical Concept Detection and Interpretable Caption Generation
CLEF 2025 Working Notes (CEUR-WS)
WisPerMed @ PerAnsSumm 2025: Strong Reasoning Through Structured Prompting and Careful Answer Selection Enhances Perspective Extraction and Summarization of Healthcare Forum Threads
CL4Health Workshop @ ACL 2025 — 1st Place
Interested in Collaborating?
I'm always open to research collaborations in NLP, healthcare AI, and medical informatics. Let's connect and explore how we can work together.
Get in Touch