Meta-Analysis on Comparison of Diagnostic Accuracy Between Artificial Intelligence and Healthcare Professionals

Background: Artificial intelligence (AI) can significantly enhance the efficient allocation of healthcare resources. The use of AI-driven diagnostic tests in healthcare settings supports healthcare professionals (HCPs) in diagnosis, treatment, and the prediction of patient outcomes. Methods: Relevant research studies published between 1 January 2015 and 30 August 2025 were included in this review. Randomized, retrospective, prospective, observational, comparative, and cross-sectional studies were incorporated. The PROBAST + AI tool was used to assess the risk of bias (ROB) and applicability concerns across the included studies. Results: The overall average diagnostic accuracy for AI vs. general HCPs was 81% vs. 71%. In comparisons of AI vs. non-expert HCPs, the accuracy was 95% vs. 82%. AI achieved significantly higher diagnostic accuracy than general and non-expert HCPs with odds ratios (OR) of 1.51 (95% CI: 1.17–1.96, p = 0.002) and 3.34 (95% CI: 1.13–9.86, p = 0.03), respectively. Diagnostic accuracy between AI and expert HCPs was 91% vs. 86%; AI achieved similar diagnostic accuracy to expert HCPs with an odds ratio (OR) of 0.72 (95% CI: 0.25–2.07, p = 0.54). Additionally, high levels of burden or burnout were significantly lower among healthcare professionals supported by AI compared with those working without AI. The pooled estimate yielded an OR of 1.77 (95% CI: 1.40–2.24, p < 0.00001), indicating a meaningful reduction in workload-related stress when AI tools were integrated into clinical practice. Conclusions: Based on the findings, AI demonstrates a positive impact on diagnostic accuracy and contributes to reducing the workload of healthcare professionals.