用外部医学文献增强AI诊断，提升罕见病脑部MRI识别能力
Learning to reason about rare diseases through retrieval-augmented agents
- 构建可检索医学文献的智能代理，通过相似性搜索获取证据支持诊断
- 在280种罕见病数据集上最高提升10.2%准确率，开源模型改善更显著
- 无需重新训练，适配多种大模型，提供可解释的医学依据
罕见病在医学影像中属于长尾分布，因代表性训练数据稀缺，现有AI模型表现不佳。临床实践中，放射科医生常查阅病例报告和文献应对不熟悉发现。受此启发，本文提出RADAR——一种基于检索增强的诊断推理智能体，用于脑部MRI中的罕见病检测。该方法利用句子嵌入技术对病例报告与文献进行编码，并通过FAISS实现高效相似性检索。智能体在无须额外训练的前提下，调用临床相关证据辅助诊断决策。作为与模型无关的推理模块，RADAR可无缝集成至多种大语言模型，在NOVA数据集（包含280种罕见病）上实现最高10.2%的性能提升，尤其显著改善开源模型如DeepSeek的表现。此外，检索到的实例提供了可解释、基于文献的诊断依据，凸显检索增强推理在低频疾病场景中的潜力。
原文摘要 · Abstract (English)
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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