Human oversight of erroneous clinical AI and decision support a systematic review and meta analysis

Artificial intelligence (AI) increasingly supports clinical decisions, but human oversight is protective only if clinicians recognize and resist erroneous output. We systematically reviewed controlled and real-world studies of clinician responses to incorrect automated advice. Forty-six reports represented 41 independent cohort clusters, including 40 core cohorts. Across three adjusted cohorts, clinician correctness was substantially higher with correct than incorrect automated advice (pooled OR 10.19, 95% CI 7.43–13.98; I² = 0%); a ≥ 2020 sensitivity analysis was similar but imprecise (OR 9.22, 95% CI 2.06–41.13). In four heterogeneous real-world computerized ECG studies, 26.6% of erroneous interpretations were not corrected or recognized (95% CI 6.4–65.6%; I² = 95.8%). Contemporary AI and large language model studies also documented harmful reliance but were too heterogeneous for pooling. Patient-level adverse events were reported in one retrospective cohort. Human oversight should be evaluated as an error-interception capability rather than assumed to ensure safety.