LLM驱动的医学文档分析：增强可信病理学与鉴别诊断
LLM-Driven Medical Document Analysis: Enhancing Trustworthy Pathology and Differential Diagnosis
- Computer Vision Center, Universitat Autònoma de Barcelona（计算机视觉中心，巴塞罗那自治大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出一个基于LLaMA-v3和低秩适配的医学文档分析平台，利用DDXPlus数据集优化鉴别诊断，提供可解释且隐私保护的诊断结果，性能超越现有模型。
AI中文摘要：
医学文档分析在从非结构化医疗记录中提取关键临床见解方面发挥着至关重要的作用，支持诸如鉴别诊断等关键任务。在症状重叠的情况下确定最可能的疾病需要精确的评估和深厚的医学专业知识。尽管大型语言模型（LLMs）的最新进展显著提升了医学文档分析的性能，但敏感患者数据相关的隐私问题限制了在线LLM服务在临床环境中的使用。为应对这些挑战，我们提出了一个可信的医学文档分析平台，该平台使用低秩适配（low-rank adaptation）对LLaMA-v3进行微调，专门针对鉴别诊断任务进行了优化。我们的方法利用了用于鉴别诊断的最大基准数据集DDXPlus，并在病理预测和变长鉴别诊断方面展示了优于现有方法的性能。所开发的基于Web的平台允许用户提交自己的非结构化医学文档，并获得准确、可解释的诊断结果。通过整合先进的解释性技术，该系统确保了透明和可靠的预测，增强了用户的信任和信心。广泛的评估证实，所提出的方法在预测准确性上超越了当前最先进的模型，同时在临床环境中提供了实用性。这项工作解决了对可靠、可解释且保护隐私的人工智能解决方案的迫切需求，代表了面向真实世界医疗应用的智能医学文档分析的重大进步。代码可在\href{https://github.com/leitro/Differential-Diagnosis-LoRA}{https://github.com/leitro/Differential-Diagnosis-LoRA}获取。
英文摘要：
Medical document analysis plays a crucial role in extracting essential clinical insights from unstructured healthcare records, supporting critical tasks such as differential diagnosis. Determining the most probable condition among overlapping symptoms requires precise evaluation and deep medical expertise. While recent advancements in large language models (LLMs) have significantly enhanced performance in medical document analysis, privacy concerns related to sensitive patient data limit the use of online LLMs services in clinical settings. To address these challenges, we propose a trustworthy medical document analysis platform that fine-tunes a LLaMA-v3 using low-rank adaptation, specifically optimized for differential diagnosis tasks. Our approach utilizes DDXPlus, the largest benchmark dataset for differential diagnosis, and demonstrates superior performance in pathology prediction and variable-length differential diagnosis compared to existing methods. The developed web-based platform allows users to submit their own unstructured medical documents and receive accurate, explainable diagnostic results. By incorporating advanced explainability techniques, the system ensures transparent and reliable predictions, fostering user trust and confidence. Extensive evaluations confirm that the proposed method surpasses current state-of-the-art models in predictive accuracy while offering practical utility in clinical settings. This work addresses the urgent need for reliable, explainable, and privacy-preserving artificial intelligence solutions, representing a significant advancement in intelligent medical document analysis for real-world healthcare applications. The code can be found at \href{https://github.com/leitro/Differential-Diagnosis-LoRA}{https://github.com/leitro/Differential-Diagnosis-LoRA}.
