Scholarlink

<p>Large language models (LLMs) show promising diagnostic and triage performance, yet direct comparisons with healthcare professionals (HCPs) and collaborative effects remain limited. We conducted a systematic review and meta-analysis of studies (January 2020 to September 2025) comparing the diagnostic or triage accuracy of LLMs, HCPs, or their collaboration across seven databases. Studies using multiple-choice formats rather than open diagnostic generation were excluded. We extracted top-1, top-3, top-5, and top-10 diagnostic and triage accuracies and pooled results using multilevel random-effects models to account for nested observations. Of 10,398 studies screened, 50 met criteria, evaluating 25 different LLMs across diverse medical specialties. The relative diagnostic accuracy of LLMs versus HCPs progressively improved from 0.89 (95% CI, 0.79–1.00) for top-1 to 0.91 (0.83–1.00) for top-3, 1.04 (0.89–1.22) for top-5, and 1.17 (0.87–1.57) for top-10 diagnoses, with significant model variability. LLM-assisted HCPs outperformed HCPs alone, with relative diagnostic accuracy of 1.13 (1.00–1.27) for top-1, 1.11 (1.01–1.23) for top-3, 1.42 (1.16–1.73) for top-5, and 1.33 (0.94–1.87) for top-10 diagnoses. Triage accuracy was similar between LLMs and HCPs (1.01 [0.94–1.09]). These findings show potential for LLM integration but methodological flaws in studies necessitate rigorous real-world evaluation before clinical implementation.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月6日

查看全文
  • Mingyang Chen,
  • Yijin Wu,
  • Jiayi Ma,
  • Xinhua Jia,
  • Chen Gao,
  • Fanghui Zhao,
  • Youlin Qiao

摘要

Large language models (LLMs) show promising diagnostic and triage performance, yet direct comparisons with healthcare professionals (HCPs) and collaborative effects remain limited. We conducted a systematic review and meta-analysis of studies (January 2020 to September 2025) comparing the diagnostic or triage accuracy of LLMs, HCPs, or their collaboration across seven databases. Studies using multiple-choice formats rather than open diagnostic generation were excluded. We extracted top-1, top-3, top-5, and top-10 diagnostic and triage accuracies and pooled results using multilevel random-effects models to account for nested observations. Of 10,398 studies screened, 50 met criteria, evaluating 25 different LLMs across diverse medical specialties. The relative diagnostic accuracy of LLMs versus HCPs progressively improved from 0.89 (95% CI, 0.79–1.00) for top-1 to 0.91 (0.83–1.00) for top-3, 1.04 (0.89–1.22) for top-5, and 1.17 (0.87–1.57) for top-10 diagnoses, with significant model variability. LLM-assisted HCPs outperformed HCPs alone, with relative diagnostic accuracy of 1.13 (1.00–1.27) for top-1, 1.11 (1.01–1.23) for top-3, 1.42 (1.16–1.73) for top-5, and 1.33 (0.94–1.87) for top-10 diagnoses. Triage accuracy was similar between LLMs and HCPs (1.01 [0.94–1.09]). These findings show potential for LLM integration but methodological flaws in studies necessitate rigorous real-world evaluation before clinical implementation.