Large Language Models in Medical Diagnostics: Scoping Review With Bibliometric Analysis

作者：Hankun Su, Yuanyuan Sun, Ruiting Li, Aozhe Zhang, Yuemeng Yang, Fen Xiao, Zhiying Duan, Jingjing Chen, Qin Hu, Tianli Yang, Bin Xu, Qiong Zhang, Jing Zhao, Yanping Li, Hui Li · 发表于：Journal of Medical Internet Research · 年份：2025 · DOI：10.2196/72062 · 被引用次数：53 · 研究领域：Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare、Genomics and Rare Diseases

BACKGROUND: The integration of large language models (LLMs) into medical diagnostics has garnered substantial attention due to their potential to enhance diagnostic accuracy, streamline clinical workflows, and address health care disparities. However, the rapid evolution of LLM research necessitates a comprehensive synthesis of their applications, challenges, and future directions. OBJECTIVE: This scoping review aimed to provide an overview of the current state of research regarding the use of LLMs in medical diagnostics. The study sought to answer four primary subquestions, as follows: (1) Which LLMs are commonly used? (2) How are LLMs assessed in diagnosis? (3) What is the current performance of LLMs in diagnosing diseases? (4) Which medical domains are investigating the application of LLMs? METHODS: This scoping review was conducted according to the Joanna Briggs Institute Manual for Evidence Synthesis and adheres to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Relevant literature was searched from the Web of Science, PubMed, Embase, IEEE Xplore, and ACM Digital Library databases from 2022 to 2025. Articles were screened and selected based on predefined inclusion and exclusion criteria. Bibliometric analysis was performed using VOSviewer to identify major research clusters and trends. Data extraction included details on LLM types, application domains, and performance metrics. RESULTS: The field is rapidl...