Learning to reason about rare diseases through retrieval-augmented agents

• URL: http://arxiv.org/abs/2511.04720v1
• Date: Thu, 06 Nov 2025 10:27:52 GMT
• Title: Learning to reason about rare diseases through retrieval-augmented agents
• Authors: Ha Young Kim, Jun Li, Ana Beatriz Solana, Carolin M. Pirkl, Benedikt Wiestler, Julia A. Schnabel, Cosmin I. Bercea,
• Abstract summary: RADAR is an agentic system for rare disease detection in brain MRI.<n>Our approach uses AI agents with access to external medical knowledge by embedding both case reports and literature.<n>The agent retrieves clinically relevant evidence to guide diagnostic decision making on unseen diseases, without the need of additional training.
• Score: 10.380898352925213
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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