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              • diagnostic-accuracy
                • 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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            • Surrogate-Endpoint Validity in Dermatology AI — Structured Literature Review
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  • Haenssle 2020 — Man against machine reloaded: market-approved CNN (Moleanalyzer Pro) vs 96 dermatologists

Haenssle 2020 — Man against machine reloaded: market-approved CNN (Moleanalyzer Pro) vs 96 dermatologists

Citation​

Haenssle HA, Fink C, Toberer F, Winkler J, Stolz W, Deinlein T, et al. Man against machine reloaded: performance of a market-approved convolutional neural network in classifying a broad spectrum of skin lesions in comparison with 96 dermatologists working under less artificial conditions. Ann Oncol. 2020 Jan;31(1):137–143. DOI: 10.1016/j.annonc.2019.10.013.

Study design and population​

Two-level comparative reader study; CE-marked CNN Moleanalyzer Pro (FotoFinder) vs. 96 dermatologists across beginner, skilled and expert tiers; 100 pigmented and non-pigmented lesion cases with clinical close-ups, dermoscopy and textual context. Multinational European reader group.

Reported metrics​

  • Level I dermatologist sensitivity 83.8 % (95 % CI 81.8–85.8); specificity 77.6 % (95 % CI 75.2–80.0)
  • Level II dermatologist sensitivity 90.6 % (95 % CI 89.3–92.0); specificity 82.4 % (95 % CI 80.5–84.3)
  • CNN sensitivity 95.0 % (95 % CI 83.5–98.6); specificity 76.7 % (95 % CI 64.6–85.6); AUC 0.918 (95 % CI 0.866–0.970)

Surrogate-to-outcome linkage​

Because the device tested is a CE-marked medical device operating under realistic dermoscopic-plus-context conditions, accuracy metrics directly parallel a regulatory-grade diagnostic-accuracy surrogate. AI assistance benefits less-experienced users most — the operational mechanism by which a Class IIb CDS device raises appropriate-biopsy and referral rates when deployed at primary-care level.

CRIT1–7 appraisal​

CriterionScoreJustification
CRIT1 Relevance3CE-marked dermatology CNN; directly analogous regulatory context.
CRIT2 Methodology296-reader prospective design across three experience tiers; reference standard histopathology.
CRIT3 Reporting3Point estimates with 95 % CIs reported for sensitivity, specificity and AUC.
CRIT4 Applicability3Highly applicable — CE-marked device, "less artificial" conditions, intended-use population.
CRIT5 Evidence weight2Large prospective multi-reader study on market-approved device.
CRIT6 Risk of bias2100-case curated dataset; limited phototype diversity; manufacturer co-authorship flag.
CRIT7 Contribution3Highest-applicability reference — demonstrates regulatory-grade diagnostic performance of a commercial CE-marked CNN.

Aggregate: very strong.

Limitations and notes​

Manufacturer co-authorship; 100-case test; curated phototype distribution; no patient-outcome follow-up.

Strength as anchor​

Very strong — reference of choice for the regulator-facing argument that a Class IIb dermatology CDS can meet diagnostic-accuracy endpoints adequate for safe deployment. Reports 95 % CIs, increasing CRIT3 score relative to Esteva 2017 / Haenssle 2018.

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Han 2018 — Clinical-image classification for benign and malignant tumours (cross-ethnicity) [BALANCING]
  • Citation
  • Study design and population
  • Reported metrics
  • Surrogate-to-outcome linkage
  • CRIT1–7 appraisal
  • Limitations and notes
  • Strength as anchor
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