How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review

How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? An Umbrella Review Lydia J. Sollis, Arianna Bunnell, Eujin Cho, Mark L. Willingham Jr., and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9069373/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background AI algorithms for skin cancer detection have shown performance comparable to clinicians in controlled settings, yet their real-world reliability, performance across diverse populations, and readiness for clinical deployment remain uncertain. This umbrella review synthesizes evidence across the screening pathway to characterize AI performance, identify equity gaps, and assess implementation readiness. Methods We searched PubMed, Web of Science, and CINAHL (November 6, 2024) for systematic reviews and meta-analyses evaluating AI for skin cancer detection, excluding narrative reviews, scoping reviews, and reviews not reporting diagnostic accuracy. Two investigators (LS, AB) independently screened studies and assessed quality using ROBIS; one (LS) extracted data with verification by a second (AB). Findings were synthesized narratively by screening phase. This study is registered with PROSPERO (CRD42024605934). Results Of 411 records identified, 37 (2008–2024) met inclusion criteria; 10 (27.0%) were judged low risk of bias, 22 (59.5%) high, and five (13.5%) unclear. Self-screening applications demonstrated marked performance variability (sensitivity 0–98%), with reduced sensitivity for melanoma detection reported across reviews. Primary care AI achieved moderate accuracy (sensitivity 60–84%, specificity 88–93%). Specialist dermoscopy-based AI achieved sensitivities comparable to dermatologists (82–91%), and histopathology AI achieved 90% sensitivity. AI augmentation increased clinician sensitivity by 6–8 percentage points, with greater benefit for generalists (+28%) than specialists (+2%). Engagement with skin tone and ethnicity increased but remained largely superficial, and >70% of datasets were from light-skinned populations. Evidence disproportionately targeted melanoma (>40% of reviews) despite it comprising <2% of skin cancers; no reviews employed implementation science frameworks. Conclusions Current evidence does not support unsupervised clinical deployment of AI-based skin cancer detection. Self-screening tools demonstrate inconsistent performance, equity gaps persist, and common non-melanoma skin cancers remain understudied. These findings support the need for stage-specific validation standards and performance reporting. Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Health sciences/Health care Health sciences/Medical research Health sciences/Oncology Full Text Additional Declarations No competing interests reported. Supplementary Files UmbrellaReviewSupplementaryMaterial3826.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 24 Mar, 2026 Reviews received at journal 23 Mar, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers invited by journal 23 Mar, 2026 Editor assigned by journal 13 Mar, 2026 Submission checks completed at journal 12 Mar, 2026 First submitted to journal 09 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9069373","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":608269984,"identity":"53b898aa-8168-40a1-9771-77fe644b8fc4","order_by":0,"name":"Lydia J. 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This umbrella review synthesizes evidence across the screening pathway to characterize AI performance, identify equity gaps, and assess implementation readiness.\u003c/p\u003e\n\u003cp\u003eMethods\u003c/p\u003e\n\u003cp\u003eWe searched PubMed, Web of Science, and CINAHL (November 6, 2024) for systematic reviews and meta-analyses evaluating AI for skin cancer detection, excluding narrative reviews, scoping reviews, and reviews not reporting diagnostic accuracy. Two investigators (LS, AB) independently screened studies and assessed quality using ROBIS; one (LS) extracted data with verification by a second (AB). Findings were synthesized narratively by screening phase. This study is registered with PROSPERO (CRD42024605934).\u003c/p\u003e\n\u003cp\u003eResults\u003c/p\u003e\n\u003cp\u003eOf 411 records identified, 37 (2008–2024) met inclusion criteria; 10 (27.0%) were judged low risk of bias, 22 (59.5%) high, and five (13.5%) unclear. Self-screening applications demonstrated marked performance variability (sensitivity 0–98%), with reduced sensitivity for melanoma detection reported across reviews. Primary care AI achieved moderate accuracy (sensitivity 60–84%, specificity 88–93%). Specialist dermoscopy-based AI achieved sensitivities comparable to dermatologists (82–91%), and histopathology AI achieved 90% sensitivity. AI augmentation increased clinician sensitivity by 6–8 percentage points, with greater benefit for generalists (+28%) than specialists (+2%). Engagement with skin tone and ethnicity increased but remained largely superficial, and \u0026gt;70% of datasets were from light-skinned populations. Evidence disproportionately targeted melanoma (\u0026gt;40% of reviews) despite it comprising \u0026lt;2% of skin cancers; no reviews employed implementation science frameworks.\u003c/p\u003e\n\u003cp\u003eConclusions\u003c/p\u003e\n\u003cp\u003eCurrent evidence does not support unsupervised clinical deployment of AI-based skin cancer detection. Self-screening tools demonstrate inconsistent performance, equity gaps persist, and common non-melanoma skin cancers remain understudied. These findings support the need for stage-specific validation standards and performance reporting.\u003c/p\u003e","manuscriptTitle":"How is Artificial Intelligence Transforming the Skin Cancer Screening Pathway? 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