Pathology underpins modern diagnosis and cancer care, yet its most valuable asset, the accumulated experience encoded in millions of narrative reports, remains largely inaccessible. Although institutions are rapidly digitizing pathology workflows, storing data without effective mechanisms for retrieval and reasoning risks transforming archives into a passive data repository, where institutional knowledge exists but cannot meaningfully inform patient care. True progress requires not only digitization, but the ability for pathologists to interrogate prior similar cases in real time while evaluating a new diagnostic dilemma. We present PathoScribe, a unified retrieval-augmented large language model (LLM) framework designed to transform static pathology archives into a searchable, reasoning-enabled living library. PathoScribe enables natural language case exploration, automated cohort construction, clinical question answering, immunohistochemistry (IHC) panel recommendation, and prompt-controlled report transformation within a single architecture. Evaluated on 70,000 multi-institutional surgical pathology reports, PathoScribe achieved perfect Recall@10 for natural language case retrieval and demonstrated high-quality retrieval-grounded reasoning (mean reviewer score 4.56/5). Critically, the system operationalized automated cohort construction from free-text eligibility criteria, assembling research-ready cohorts in minutes (mean 9.2 minutes) with 91.3% agreement to human reviewers and no eligible cases incorrectly excluded, representing orders-of-magnitude reductions in time and cost compared to traditional manual chart review. This work establishes a scalable foundation for converting digital pathology archives from passive storage systems into active clinical intelligence platforms.

翻译：病理学是现代诊断与癌症治疗的基石，然而其最宝贵的资产——编码于数百万份叙述性报告中的累积经验——在很大程度上仍难以被有效利用。尽管各机构正在快速推进病理工作流程的数字化，但若仅存储数据而缺乏有效的检索与推理机制，将导致档案库沦为被动的数据仓库，其中虽存有机构知识却无法实质性地指导临床诊疗。真正的进步不仅需要数字化，更需要病理医师在评估新诊断难题时能够实时查询过往类似病例。本文提出PathoScribe，一个统一的检索增强大语言模型（LLM）框架，旨在将静态病理档案转化为可检索、具备推理能力的动态知识库。PathoScribe在单一架构内实现了自然语言病例探索、自动化队列构建、临床问答、免疫组化（IHC）套餐推荐以及提示控制的报告转换等功能。基于70,000份多机构外科病理报告进行评估，PathoScribe在自然语言病例检索中实现了Recall@10的完美表现，并展现出高质量的基于检索的推理能力（平均评审得分4.56/5）。尤为关键的是，该系统实现了基于自由文本入组标准的自动化队列构建，可在数分钟内（平均9.2分钟）完成研究就绪队列的组装，与人工评审的一致性达91.3%，且无误排除合格病例，相较于传统人工病历审查，在时间与成本上实现了数量级的降低。本研究为将数字病理档案从被动存储系统转化为主动临床智能平台奠定了可扩展的基础。