DiReCT：基于大语言模型的临床笔记诊断推理
DiReCT: Diagnostic Reasoning for Clinical Notes via Large Language Models
AI总结：
本文提出临床笔记诊断推理数据集DiReCT，包含511份医生标注的临床笔记和诊断知识图谱，用于评估大语言模型在真实临床场景中的推理能力与可解释性，并揭示其与人类医生的显著差距。
摘要：
大语言模型（LLMs）近期展现了卓越的能力，涵盖广泛的任务和应用，包括医疗领域。像GPT-4这样的模型在医学问答中表现出色，但在真实临床环境中处理复杂任务时可能面临缺乏可解释性的挑战。因此，我们引入了临床笔记诊断推理数据集（DiReCT），旨在评估LLMs与人类医生相比的推理能力和可解释性。该数据集包含511份临床笔记，每份均由医生精心标注，详细描述了从临床笔记中的观察到最终诊断的诊断推理过程。此外，还提供了一个诊断知识图谱，以提供推理所需的基本知识，这些知识可能未被现有LLMs的训练数据覆盖。对领先LLMs在DiReCT上的评估揭示了它们的推理能力与人类医生之间的显著差距，突显了对能够在真实临床场景中有效推理的模型的迫切需求。
英文摘要：
Large language models (LLMs) have recently showcased remarkable capabilities, spanning a wide range of tasks and applications, including those in the medical domain. Models like GPT-4 excel in medical question answering but may face challenges in the lack of interpretability when handling complex tasks in real clinical settings. We thus introduce the diagnostic reasoning dataset for clinical notes (DiReCT), aiming at evaluating the reasoning ability and interpretability of LLMs compared to human doctors. It contains 511 clinical notes, each meticulously annotated by physicians, detailing the diagnostic reasoning process from observations in a clinical note to the final diagnosis. Additionally, a diagnostic knowledge graph is provided to offer essential knowledge for reasoning, which may not be covered in the training data of existing LLMs. Evaluations of leading LLMs on DiReCT bring out a significant gap between their reasoning ability and that of human doctors, highlighting the critical need for models that can reason effectively in real-world clinical scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
