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Kim, H.Y.* ; Li, J.* ; Solana, A.B.* ; Pirkl, C.M.* ; Wiestler, B.* ; Schnabel, J.A. ; Bercea, C.-I.

Learning to reason about rare diseases through retrieval-augmented agents.

In: (23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026, 8-11 April 2026, London). 2026. (Proceedings International Symposium on Biomedical Imaging ; 2026-April)
DOI
Rare diseases represent the long tail of medical imaging, where AI models often fail due to the scarcity of representative training data. In clinical workflows, radiologists frequently consult case reports and literature when confronted with unfamiliar findings. Following this line of reasoning, we introduce RADAR (Retrieval-Augmented Diagnostic Reasoning Agents), an agentic system for rare disease detection in brain MRI. Our approach uses AI agents with access to external medical knowledge by embedding both case reports and literature using sentence transformers and indexing them with FAISS to enable efficient similarity search. The agent retrieves clinically relevant evidence to guide diagnostic decision-making on unseen diseases, without the need of additional training. Designed as a model-agnostic reasoning module, RADAR can be seamlessly integrated with diverse large-language models, consistently improving their rare pathology recognition and interpretability. On the NOVA dataset comprising 280 distinct rare diseases, RADAR achieves up to a 10.2% performance gain, with the strongest improvements observed for open-source models such as DeepSeek. Beyond accuracy, the retrieved examples provide interpretable, literature-grounded explanations, highlighting retrieval-augmented reasoning as a powerful paradigm for low-prevalence conditions in medical imaging. Code and Details: https://github.com/ha-y0ung-kim/RADAR.
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Publikationstyp Artikel: Konferenzbeitrag
Schlagwörter Agentic Ai ; Brain Disorders ; Disease Diagnosis ; Medical Imaging ; Retreival Augmented Generation
ISSN (print) / ISBN 1945-7928
e-ISSN 1945-8452
Konferenztitel 23rd IEEE International Symposium on Biomedical Imaging, ISBI 2026
Konferzenzdatum 8-11 April 2026
Konferenzort London
Quellenangaben Band: 2026-April Heft: , Seiten: , Artikelnummer: , Supplement: ,
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)