首篇系统综述大模型在疾病诊断中的应用与挑战
Large Language Models for Disease Diagnosis: A Scoping Review
- 梳理大模型用于诊断的疾病类型、数据源和评估方法
- 揭示当前研究覆盖范围有限，缺乏统一评估标准
- 适合医疗AI研究者与临床决策系统开发者参考
自动疾病诊断在临床实践中日益重要。大语言模型（LLMs）的兴起推动了人工智能范式变革，越来越多证据表明其在诊断任务中的有效性。尽管该领域关注度上升，但整体认知仍不完整。许多关键问题尚不明确，包括大模型应用于哪些疾病和临床数据、采用的技术方法以及评估方式。本文对基于大模型的疾病诊断方法进行综合性综述，从疾病种类与相关临床专科、临床数据、大模型技术及评估方法等多个维度分析现有文献。同时，提出大模型应用于诊断任务的实践建议，并评估当前研究局限，探讨未来方向。据我们所知，这是首个针对大模型疾病诊断的全面综述。
原文摘要 · Abstract (English)
Automatic disease diagnosis has become increasingly valuable in clinical practice. The advent of large language models (LLMs) has catalyzed a paradigm shift in artificial intelligence, with growing evidence supporting the efficacy of LLMs in diagnostic tasks. Despite the increasing attention in this field, a holistic view is still lacking. Many critical aspects remain unclear, such as the diseases and clinical data to which LLMs have been applied, the LLM techniques employed, and the evaluation methods used. In this article, we perform a comprehensive review of LLM-based methods for disease diagnosis. Our review examines the existing literature across various dimensions, including disease types and associated clinical specialties, clinical data, LLM techniques, and evaluation methods. Additionally, we offer recommendations for applying and evaluating LLMs for diagnostic tasks. Furthermore, we assess the limitations of current research and discuss future directions. To our knowledge, this is the first comprehensive review for LLM-based disease diagnosis.
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