Can Artificial Intelligence Match Dermoscopy in Melanoma Detection? Evidence from a Systematic Review and Meta-analysis of Pigmented Skin Lesions

Accurate risk stratification of pigmented skin lesions is critical for early melanoma detection and for reducing unnecessary excisions. Artificial intelligence (AI) is increasingly applied to dermoscopic image analysis, but its diagnostic performance relative to standard dermoscopy in real-world clinical settings remains uncertain. To address this gap, we conducted a systematic review and meta-analysis of prospective clinical studies directly comparing AI alone, dermoscopy, and AI-assisted clinicians for malignancy risk assessment of pigmented skin lesions. We systematically searched PubMed, Embase, Web of Science, and Cochrane Library from inception to January 2026. Ten studies with 17 diagnostic arms (10 dermoscopy arms, 6 AI-alone arms, and 1 AI-assisted clinician arm) were included. Pooled sensitivity and specificity were 0.773 (95% CI, 0.648-0.863) and 0.793 (95% CI, 0.673-0.877) for dermoscopy, and 0.757 (95% CI, 0.428-0.928) and 0.859 (95% CI, 0.619-0.958) for standalone AI. Summary ROC curves showed overlapping performance, indicating that autonomous AI is broadly comparable to dermoscopy but does not demonstrate a consistent advantage. Heterogeneity in AI performance was driven almost entirely by threshold effects rather than by differences in inherent model capacity. AI-assisted clinicians showed promising results (sensitivity 1.000, specificity 0.837) in a single study, but more evidence is needed. Our findings suggest that, at present, AI should be viewed as a complementary decision-support tool rather than a replacement for dermoscopic evaluation. The study provides valuable evidence for clinicians, guideline developers, and researchers working on AI integration into melanoma diagnostic pathways.