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  • Salinas 2024 — Systematic review and meta-analysis of AI vs. clinicians for skin cancer diagnosis

Salinas 2024 — Systematic review and meta-analysis of AI vs. clinicians for skin cancer diagnosis

Citation​

Salinas MP, Sepúlveda J, Hidalgo L, Peirano D, Morel M, Uribe P, et al. A systematic review and meta-analysis of artificial intelligence versus clinicians for skin cancer diagnosis. NPJ Digit Med. 2024 May 14;7(1):125. DOI: 10.1038/s41746-024-01103-x. PMID 38744955.

Study design and population​

Pre-registered systematic review and meta-analysis (PRISMA 2020, QUADAS-2); 53 comparative studies screened; 19 included in bivariate meta-analysis. AI algorithms vs. clinicians for benign/malignant classification against histopathology reference.

Reported metrics​

  • Pooled AI sensitivity 87.0 % (95 % CI 81.7–90.9); specificity 77.1 % (95 % CI 69.8–83.0)
  • All clinicians — sensitivity 79.8 % (95 % CI 73.2–85.1); specificity 73.6 % (95 % CI 66.5–79.6)
  • AI vs. expert dermatologists — clinically comparable (AI sens 86.3 %, spec 78.4 % vs. expert sens 84.2 %, spec 74.4 %)
  • AI vs. generalists — AI markedly superior in sensitivity (92.5 % vs. 64.6 %)

Surrogate-to-outcome linkage​

Confirms at meta-analytic level that AI diagnostic accuracy is comparable to expert dermatologists and significantly superior to non-specialists, who constitute the initial point of contact for most skin-lesion presentations. Validates sensitivity/specificity as a surrogate for clinically relevant triage accuracy at the primary-care care-step.

CRIT1–7 appraisal​

CriterionScoreJustification
CRIT1 Relevance3Direct — AI vs clinicians for skin-cancer diagnosis.
CRIT2 Methodology3PRISMA 2020, pre-registered, QUADAS-2, bivariate meta-analysis.
CRIT3 Reporting3Pooled sensitivity/specificity with 95 % CIs by comparator subgroup.
CRIT4 Applicability3Subgroup analyses (expert vs. generalist) match the intended-use clinical context.
CRIT5 Evidence weight3Meta-analysis — highest tier.
CRIT6 Risk of bias2QUADAS-2 concerns in constituent studies; predominantly curated-dataset evaluations.
CRIT7 Contribution3Contemporary anchor for accepted-surrogate + directional claims; adds generalist-comparator data that Dick 2019 lacks.

Aggregate: very strong.

Limitations and notes​

Constituent studies largely use curated datasets; phototype coverage inconsistently reported; no direct patient-outcome linkage.

Strength as anchor​

Very strong — complements Dick 2019 with more recent data (search to August 2022) and an explicit expert-vs-generalist comparator stratification that anchors the CDS primary-care-uplift argument.

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Tschandl 2020 — Human–computer collaboration for skin cancer recognition
  • Citation
  • Study design and population
  • Reported metrics
  • Surrogate-to-outcome linkage
  • CRIT1–7 appraisal
  • Limitations and notes
  • Strength as anchor
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