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                • Conic 2018 — Impact of melanoma surgical timing on survival (NCDB)
                • Daneshjou 2022 — Disparities in dermatology AI performance on a diverse clinical image set (DDI) [BALANCING]
                • Dick 2019 — Accuracy of computer-aided diagnosis of melanoma: a meta-analysis
                • Esteva 2017 — Dermatologist-level classification of skin cancer with deep neural networks
                • Freeman 2020 — Algorithm-based smartphone apps for skin cancer risk: BMJ systematic review [BALANCING]
                • Gershenwald 2017 — AJCC 8th edition: melanoma staging and survival gradient
                • Haenssle 2018 — Man against machine: CNN vs 58 dermatologists for melanoma recognition
                • Haenssle 2020 — Man against machine reloaded: market-approved CNN (Moleanalyzer Pro) vs 96 dermatologists
                • Han 2018 — Clinical-image classification for benign and malignant tumours (cross-ethnicity) [BALANCING]
                • Liu 2020 — A deep learning system for differential diagnosis of skin diseases
                • Salinas 2024 — Systematic review and meta-analysis of AI vs. clinicians for skin cancer diagnosis
                • Tschandl 2020 — Human–computer collaboration for skin cancer recognition
                • Winkler 2023 — Dermatologists cooperating with a CNN: prospective clinical study
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  • Winkler 2023 — Dermatologists cooperating with a CNN: prospective clinical study

Winkler 2023 — Dermatologists cooperating with a CNN: prospective clinical study

Citation​

Winkler JK, Blum A, Kommoss K, Enk A, Toberer F, Rosenberger A, Haenssle HA. Assessment of Diagnostic Performance of Dermatologists Cooperating With a Convolutional Neural Network in a Prospective Clinical Study: Human With Machine. JAMA Dermatol. 2023 Jun 1;159(6):621–627. DOI: 10.1001/jamadermatol.2023.0905. PMID 37133847.

Study design and population​

Prospective two-centre clinical study. 22 dermatologists evaluated 228 suspect melanocytic lesions with and without market-approved CNN support (Moleanalyzer Pro, FotoFinder). Histopathological reference available for 54.8 % of lesions.

Reported metrics​

  • Sensitivity: dermatologist alone 84.2 % (95 % CI 69.6–92.6) → with CNN 100.0 % (95 % CI 90.8–100.0); p = 0.03
  • Specificity: 72.1 % → 83.7 %; p < 0.001
  • ROC AUC: 0.895 (95 % CI 0.836–0.954) → 0.968 (95 % CI 0.948–0.988); p = 0.005
  • CNN guidance reduced unnecessary excision of benign nevi by 19.2 %

Surrogate-to-outcome linkage​

Prospective, real-world evidence that AI-assisted diagnostic accuracy translates into a measurable reduction in unnecessary procedures (19.2 % fewer benign excisions) while simultaneously eliminating missed melanomas. Closes the loop from accuracy surrogate to the patient-relevant iatrogenic-harm outcome.

CRIT1–7 appraisal​

CriterionScoreJustification
CRIT1 Relevance3Prospective clinical study of CE-marked CNN in the intended clinician-supervised workflow.
CRIT2 Methodology3Prospective, two-centre; within-subject before-after design; histopathology reference.
CRIT3 Reporting3Sensitivity, specificity, AUC with 95 % CIs and p-values reported.
CRIT4 Applicability3Direct match — dermatologist + CNN in real clinical workflow.
CRIT5 Evidence weight2Prospective clinical study (not RCT, not meta-analysis).
CRIT6 Risk of bias2Within-subject design; two-centre; histopathology available for 54.8 % only.
CRIT7 Contribution3Core anchor — links accuracy uplift to reduced benign excisions, a patient-relevant outcome.

Aggregate: very strong.

Limitations and notes​

Two-centre design; histopathology partial; industry-affiliated device developer in author list.

Strength as anchor​

Very strong — one of the few prospective real-world studies quantifying the patient-relevant outcome (avoided benign excisions) downstream of AI-supported accuracy. Complements Tschandl 2020 (simulated reader) with real-deployment evidence.

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Armstrong 2018 — Online vs in-person care for psoriasis: equivalency RCT
  • Citation
  • Study design and population
  • Reported metrics
  • Surrogate-to-outcome linkage
  • CRIT1–7 appraisal
  • Limitations and notes
  • Strength as anchor
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