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Human–large language model collaboration in clinical medicine: a systematic review and meta-analysis
Wang, Guoyong Zhang, Kaijun Jiang, Jiyue Wang, Chaonan Bi, Hui Liang, Haojun Qi, Zuoliang Huang, Ying Li, Yu Yang, Xiaonan
Wang, Guoyong
Zhang, Kaijun
Jiang, Jiyue
Wang, Chaonan
Bi, Hui
Liang, Haojun
Qi, Zuoliang
Huang, Ying
Li, Yu
Yang, Xiaonan
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s41746-026-02382-2.pdf
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Computational Biology
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Journal article
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http://creativecommons.org/licenses/by/4.0/
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English
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Abstract
Human–AI collaboration (H + AI) using large language models (LLMs) offers a promising approach to enhance clinical reasoning, documentation, and interpretation tasks. Following PRISMA 2020 (PROSPERO registration: CRD420251068272), we systematically compared H + AI with human-only (H) workflows, searching four databases through June 28, 2025. Ten peer-reviewed studies met eligibility criteria, with three preprints informing sensitivity analyses only. Diagnostic/interpretation accuracy (k = 2) showed a positive trend for H + AI (Risk Ratio [RR] 1.59), but was statistically imprecise and non-significant (95% CI 0.08 to 32.74), with 95% prediction intervals (PI) crossing the null. Composite diagnostic/management scores (k = 2) showed a statistically significant improvement (Mean Difference [MD] +4.88 percentage points, 95% CI + 0.65 to +9.12), yet the PI (–31.65 to 41.42) indicates high real-world uncertainty. Time efficiency (k = 3) showed no overall difference (MD + 0.4 min, 95%CI −4.18 to +4.97; I² = 70.1%). While documentation quality improved, but factual error rates remained high (~26–36%), undermining quality gains. In three-arm settings, H + AI did not universally outperform AI-only. Evidence remains preliminary yet highly uncertain and context-dependent. We recommend preregistered, pragmatic, multicenter trials embedded in real workflows, with harmonized core outcomes that prioritize safety/error metrics and interfaces that surface uncertainty and support verification.
Citation
G. Wang, K. Zhang, J. Jiang, C. Wang, H. Bi, H. Liang , et al., "Human–large language model collaboration in clinical medicine: a systematic review and meta-analysis," npj Digital Medicine, vol. 9, no. 1, pp. 195-195, 2026, https://doi.org/10.1038/s41746-026-02382-2.
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npj Digital Medicine
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42 Health Sciences, 4203 Health Services and Systems, 3 Good Health and Well Being
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Springer Nature