AI Matches Expert Dermatologists but Significantly Outperforms Generalists in Skin Cancer Diagnosis
A meta-analysis of 53 studies comparing AI algorithms with clinicians for skin cancer classification found that AI achieved 87.0% sensitivity and 77.1% specificity overall, significantly outperforming clinicians across all subgroups. The performance gap was largest between AI and generalist clinicians, while AI and expert dermatologists performed comparably. The findings support AI as a diagnostic aid particularly in settings without dermatology expertise, though real-world validation remains needed.
The original study
A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.
- Authors
- Salinas MP, Sepúlveda J, Hidalgo L, Peirano D, Morel M, Uribe P, et al.
- Journal
- NPJ digital medicine
- Type
- Journal Article, Review
- PMID
- 38744955
Original abstract
Scientific research of artificial intelligence (AI) in dermatology has increased exponentially. The objective of this study was to perform a systematic review and meta-analysis to evaluate the performance of AI algorithms for skin cancer classification in comparison to clinicians with different levels of expertise. Based on PRISMA guidelines, 3 electronic databases (PubMed, Embase, and Cochrane Library) were screened for relevant articles up to August 2022. The quality of the studies was assessed using QUADAS-2. A meta-analysis of sensitivity and specificity was performed for the accuracy of AI and clinicians. Fifty-three studies were included in the systematic review, and 19 met the inclusion criteria for the meta-analysis. Considering all studies and all subgroups of clinicians, we found a sensitivity (Sn) and specificity (Sp) of 87.0% and 77.1% for AI algorithms, respectively, and a Sn of 79.78% and Sp of 73.6% for all clinicians (overall); differences were statistically significant for both Sn and Sp. The difference between AI performance (Sn 92.5%, Sp 66.5%) vs. generalists (Sn 64.6%, Sp 72.8%), was greater, when compared with expert clinicians. Performance between AI algorithms (Sn 86.3%, Sp 78.4%) vs expert dermatologists (Sn 84.2%, Sp 74.4%) was clinically comparable. Limitations of AI algorithms in clinical practice should be considered, and future studies should focus on real-world settings, and towards AI-assistance.