RareDxR1：超越人类标注的罕见病诊断自主医学推理
RareDxR1: Autonomous Medical Reasoning for Rare Disease Diagnosis Beyond Human Annotation
AI总结：
提出端到端推理大模型RareDxR1，通过知识内化与自主进化学习框架，结合反思增强推理采样和双级课程强化学习，直接从非结构化临床笔记诊断罕见病，无需依赖结构化表型或人类标注，在多个基准上达到最先进准确率。
AI中文摘要：
罕见病鉴别诊断是一项关键但艰巨的临床任务，要求医生从复杂、非结构化的患者症状中识别精确表型，并在广阔的搜索空间中进行复杂推理。然而，现有的人工智能方法通常依赖于基于流水线的表型提取或检索增强生成，由于预定义本体、检索瓶颈和缺乏诊断逻辑，这些方法会导致关键信息丢失。为应对这些挑战，我们提出了RareDxR1，一个端到端的以推理为中心的大语言模型，旨在直接从非结构化临床笔记进行开放域罕见病诊断。我们设计了一个渐进式端到端训练框架，通过协同知识内化与自主进化学习，从而绕过对结构化表型和封闭集决策的依赖。为了克服RAG和表型限制，我们实现了将碎片化的罕见病知识直接深度内化到模型参数中。此外，为了弥合模型生成与专家推理之间的差距，我们提出了反思增强推理采样（RERS）策略，该策略通过从失败中学习而无需人类标注来合成专家级诊断轨迹。另外，我们提出了一种双级课程强化学习方法，用于逐步掌握罕见病诊断。实验结果表明，RareDxR1在不同基准上均达到了最先进的准确率，标志着开放域罕见病诊断的重大突破。我们的代码和数据集将公开提供。
英文摘要：
Rare disease differential diagnosis is a critical yet arduous clinical task, requiring physicians to identify precise phenotypes from complex, unstructured patient symptoms and execute intricate reasoning within a vast search space. However, existing AI approaches typically rely on pipeline-based phenotype extraction or retrieval-augmented generation, which suffer from critical information loss due to predefined ontologies, retrieval bottlenecks, and a lack of diagnostic logic. To address these challenges, we introduce RareDxR1, an end-to-end reasoning-centric large language model designed for open-domain rare disease diagnosis directly from unstructured clinical notes. We design a progressive end-to-end training framework by synergizing knowledge internalization with autonomous evolutionary learning, thereby bypassing reliance on structured phenotypes and closed-set decision-making. To overcome the limitations of RAG and phenotype restriction, we enabled the deep internalization of fragmented rare-disease knowledge directly into the model's parameters. Moreover, to bridge the gap between model generation and expert reasoning, we propose Reflection-Enhanced Reasoning Sampling (RERS), a strategy that synthesizes expert-level diagnostic trajectories by learning from failures without human annotation. Additionally, we propose a dual-level curriculum reinforcement learning approach for gradually mastering rare disease diagnosis. Experimental results demonstrate that RareDxR1 achieves state-of-the-art accuracy across different benchmarks, marking a significant breakthrough in open-domain rare disease diagnosis. Our code and dataset will be publicly available.
