用AI提升甲状腺细针穿刺诊断准确率，让系统更懂病理。
Enhancing Thyroid Cytology Diagnosis with RAG-Optimized LLMs and Pa-thology Foundation Models
- 结合检索增强生成与病理基础模型，动态调取诊断知识
- 在甲状腺穿刺样本上实现0.73-0.93的AUC预测性能
- 适合病理科医生辅助诊断，尤其处理疑难病例时
人工智能进步正推动病理学变革，通过将大语言模型（LLMs）与检索增强生成（RAG）及领域专用基础模型结合。本研究探索了融合RAG增强的LLM与病理基础模型在甲状腺细针穿刺诊断中的应用，解决细胞学解读、标准化和诊断准确性挑战。借助精心构建的知识库，RAG实现相关病例、诊断标准与专家解读的动态检索，提升LLM的上下文理解能力。同时，基于高分辨率病理图像训练的病理基础模型，强化特征提取与分类能力。两者融合显著提升诊断一致性，减少变异，辅助病理医生区分良恶性甲状腺病变。结果表明，将RAG与特定病理的LLM结合可显著提高诊断效率与可解释性，为人工智能辅助甲状腺细胞病理学铺路，其中基础模型UNI在从甲状腺穿刺样本预测手术病理诊断中取得0.73-0.93的AUC值。
原文摘要 · Abstract (English)
Advancements in artificial intelligence (AI) are transforming pathology by integrat-ing large language models (LLMs) with retrieval-augmented generation (RAG) and domain-specific foundation models. This study explores the application of RAG-enhanced LLMs coupled with pathology foundation models for thyroid cytology diagnosis, addressing challenges in cytological interpretation, standardization, and diagnostic accuracy. By leveraging a curated knowledge base, RAG facilitates dy-namic retrieval of relevant case studies, diagnostic criteria, and expert interpreta-tion, improving the contextual understanding of LLMs. Meanwhile, pathology foun-dation models, trained on high-resolution pathology images, refine feature extrac-tion and classification capabilities. The fusion of these AI-driven approaches en-hances diagnostic consistency, reduces variability, and supports pathologists in dis-tinguishing benign from malignant thyroid lesions. Our results demonstrate that integrating RAG with pathology-specific LLMs significantly improves diagnostic efficiency and interpretability, paving the way for AI-assisted thyroid cytopathology, with foundation model UNI achieving AUC 0.73-0.93 for correct prediction of surgi-cal pathology diagnosis from thyroid cytology samples.
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