Learning to Reason About Rare Diseases Through Retrieval-Augmented Agents

MCML Authors

Jun Li

→ Group Julia Schnabel
 Computational Imaging and AI in Medicine

Benedikt Wiestler

Prof. Dr.

Principal Investigator

AI for Image-Guided Diagnosis and Therapy

Julia Schnabel

Prof. Dr.

Principal Investigator

Computational Imaging and AI in Medicine

Cosmin Bercea

Dr.

→ Group Benedikt Wiestler
 AI for Image-Guided Diagnosis and Therapy

Abstract

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.

misc KLS+25

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Preprint

Nov. 2025

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Authors

H. Y. Kim • J. Li • A. B. Solana • C. M. Pirkl • B. Wiestler • J. A. Schnabel • C. I. Bercea

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Links

arXiv

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Research Area

 C1 | Medicine