The Effect of LLM Assistance on Diagnostic Accuracy: A Systematic Review and Meta-Analysis

Large language models (LLMs) are increasingly used in clinical settings, yet their effect on diagnostic accuracy of physicians has not been systematically quantified. We conducted a systematic review and meta-analysis of peer-reviewed articles and preprints published between January 2020 and June 2026 comparing diagnostic accuracy of physicians with and without LLM assistance. Eligible studies were randomized or non-randomized comparative studies using parallel-group or within-subject designs. We assessed potential risk of bias via PROBAST. Across 42 studies and 101 effect sizes, LLM assistance was associated with a small improvement in diagnostic accuracy (Hedges g = 0.22, 95% CI [0.18, 0.27], p < 0.001). However, the magnitude of the improvement varied substantially across subgroups (e.g., across LLMs or medical fields). These findings show that LLMs can improve the diagnostic accuracy of physicians, but conditions for successful LLM assistance remain unclear. Further clinical evidence is needed to guide safe and effective integration into practice.