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A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis.

Maria Paz SalinasJaviera SepúlvedaLeonel HidalgoDominga PeiranoMacarena MorelPablo UribeVeronica M RotembergJuan BrionesDomingo MeryCristian Navarrete-Dechent
Published in: NPJ digital medicine (2024)
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.
Keyphrases
  • artificial intelligence
  • machine learning
  • deep learning
  • big data
  • systematic review
  • skin cancer
  • clinical practice
  • palliative care
  • meta analyses
  • case control
  • randomized controlled trial
  • structural basis