{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:28:43Z","timestamp":1792798123716,"version":"4.5.0"},"reference-count":29,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T00:00:00Z","timestamp":1765324800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Medicina"],"abstract":"<jats:p>Background and Objectives: Artificial intelligence (AI) has shown promising performance in skin-lesion classification; however, its fairness, external validity, and real-world reliability remain uncertain. This systematic review and meta-analysis evaluated the diagnostic accuracy, equity, and generalizability of AI-based dermatology systems across diverse imaging modalities and clinical settings. Materials and Methods: A comprehensive search of PubMed, Embase, Web of Science, and ClinicalTrials.gov (inception\u201331 October 2025) identified diagnostic accuracy studies using clinical, dermoscopic, or smartphone images. Eighteen studies (11 melanoma-focused; 7 mixed benign\u2013malignant) met inclusion criteria. Six studies provided complete 2 \u00d7 2 contingency data for bivariate Reitsma HSROC modeling, while seven reported AUROC values with extractable variance. Risk of bias was assessed using QUADAS-2, and evidence certainty was graded using GRADE. Results: Across more than 70,000 test images, pooled sensitivity and specificity were 0.91 (95% CI 0.74\u20130.97) and 0.64 (95% CI 0.47\u20130.78), respectively, corresponding to an HSROC AUROC of 0.88 (95% CI 0.84\u20130.92). The AUROC-only meta-analysis yielded a similar pooled AUROC of 0.88 (95% CI 0.87\u20130.90). Diagnostic performance was highest in specialist settings (AUROC 0.90), followed by community care (0.85) and smartphone environments (0.81). Notably, performance was lower in darker skin tones (Fitzpatrick IV\u2013VI: AUROC 0.82) compared with lighter skin tones (I\u2013III: 0.89), indicating persistent fairness gaps. Conclusions: AI-based dermatology systems achieve high diagnostic accuracy but demonstrate reduced performance in darker skin tones and non-specialist environments. These findings emphasize the need for diverse training datasets, skin-tone\u2013stratified reporting, and rigorous external validation before broad clinical deployment.<\/jats:p>","DOI":"10.3390\/medicina61122186","type":"journal-article","created":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T12:20:57Z","timestamp":1765369257000},"page":"2186","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Equity and Generalizability of Artificial Intelligence for Skin-Lesion Diagnosis Using Clinical, Dermoscopic, and Smartphone Images: A Systematic Review and Meta-Analysis"],"prefix":"10.3390","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0925-366X","authenticated-orcid":false,"given":"Jeng-Wei","family":"Tjiu","sequence":"first","affiliation":[{"name":"Department of Dermatology, National Taiwan University Hospital, National Taiwan University College of Medicine, Taipei 100, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chia-Fang","family":"Lu","sequence":"additional","affiliation":[{"name":"Grace Smile Dental Clinic, Taipei 100, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2691","DOI":"10.1038\/s41591-025-03747-y","article-title":"A multimodal vision foundation model for clinical dermatology","volume":"31","author":"Yan","year":"2025","journal-title":"Nat. Med."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zbrzezny, A.M., and Krzywicki, T. (2025). Artificial intelligence in dermatology: A review of methods, clinical applications, and perspectives. Appl. Sci., 15.","DOI":"10.3390\/app15147856"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"eabq6147","DOI":"10.1126\/sciadv.abq6147","article-title":"Disparities in dermatology AI performance on a diverse, curated clinical image set","volume":"8","author":"Daneshjou","year":"2022","journal-title":"Sci. Adv."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1247","DOI":"10.1001\/jamadermatol.2018.2348","article-title":"Machine learning and health-care disparities in dermatology","volume":"154","author":"Adamson","year":"2018","journal-title":"JAMA Dermatol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e679","DOI":"10.1016\/S2589-7500(23)00130-9","article-title":"Comparison of humans versus mobile-phone-powered artificial intelligence for the diagnosis and management of pigmented skin cancer in secondary care: A multicentre, prospective, diagnostic, clinical trial","volume":"5","author":"Menzies","year":"2023","journal-title":"Lancet Digit. Health"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"102019","DOI":"10.1016\/j.eclinm.2023.102019","article-title":"Artificial intelligence in mobile health for skin cancer diagnostics at home (AIM HIGH): A pilot feasibility study","volume":"60","author":"Sangers","year":"2023","journal-title":"EClinicalMedicine"},{"key":"ref_9","first-page":"413","article-title":"Skin cancer detection in diverse skin tones by machine learning combining audio and visual convolutional neural networks","volume":"103","author":"Walker","year":"2025","journal-title":"Oncology"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1016\/j.jclinepi.2005.02.022","article-title":"Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews","volume":"58","author":"Reitsma","year":"2005","journal-title":"J. Clin. Epidemiol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2865","DOI":"10.1002\/sim.942","article-title":"A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations","volume":"20","author":"Rutter","year":"2001","journal-title":"Stat. Med."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Deeks, J.J., Bossuyt, P.M., and Gatsonis, C. (2023). Analysing and presenting results. Cochrane Handbook for Systematic Reviews of Diagnostic Test Accuracy, Version 2.0, The Cochrane Collaboration.","DOI":"10.1002\/9781119756194"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5110","DOI":"10.5826\/dpc.1503a5110","article-title":"Assessment of a smartphone-based neural network application for the risk assessment of skin lesions under real-world conditions","volume":"15","author":"Efferl","year":"2025","journal-title":"Dermatol. Pract. Concept."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann. Intern. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1106","DOI":"10.1136\/bmj.39500.677199.AE","article-title":"Grading quality of evidence and strength of recommendations for diagnostic tests and strategies","volume":"336","author":"Oxman","year":"2008","journal-title":"BMJ"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Moher, D., Liberati, A., Tetzlaff, J., Altman, D.G., and PRISMA Group (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med., 6.","DOI":"10.1371\/journal.pmed.1000097"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1093\/bjd\/ljae021","article-title":"Evaluation of an artificial-intelligence-based decision support for the detection of cutaneous melanoma in primary care: A prospective real-life clinical trial","volume":"191","author":"Papachristou","year":"2024","journal-title":"Br. J. Dermatol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.jdin.2023.10.011","article-title":"Multicenter prospective blinded melanoma detection study with a handheld elastic scattering spectroscopy device","volume":"15","author":"Hartman","year":"2024","journal-title":"JAAD Int."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1038\/s41746-023-00872-1","article-title":"Prospective validation of dermoscopy-based open-source artificial intelligence for melanoma diagnosis (PROVE-AI study)","volume":"6","author":"Marchetti","year":"2023","journal-title":"npj Digit. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1038\/s43856-024-00598-5","article-title":"Prospective multicenter study using artificial intelligence to improve dermoscopic melanoma diagnosis in patient care","volume":"4","author":"Heinlein","year":"2024","journal-title":"Commun. Med."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"AIdbp2400732","DOI":"10.1056\/AIdbp2400732","article-title":"Multimodal image dataset for AI-based skin cancer (MIDAS) benchmarking","volume":"2","author":"Chiou","year":"2025","journal-title":"NEJM AI"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4739","DOI":"10.1038\/s41467-025-59532-5","article-title":"Dermatologist-like explainable AI enhances melanoma diagnosis accuracy: Eye-tracking study","volume":"16","author":"Chanda","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"adv00204","DOI":"10.2340\/00015555-3624","article-title":"Evaluation of the diagnostic accuracy of an online artificial-intelligence application for skin-disease diagnosis","volume":"100","author":"Zaar","year":"2020","journal-title":"Acta Derm. Venereol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liutkus, J., Kriukas, A., Stragyte, D., Mazeika, E., Raudonis, V., Galetzka, W., Stang, A., and Valiukeviciene, S. (2023). Accuracy of a smartphone-based artificial intelligence application for classification of melanomas, melanocytic nevi, and seborrheic keratoses. Diagnostics, 13.","DOI":"10.3390\/diagnostics13132139"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"e39113","DOI":"10.2196\/39113","article-title":"Issues in melanoma detection: Semisupervised deep learning algorithm development via a combination of human and artificial intelligence","volume":"5","author":"Zhang","year":"2022","journal-title":"JMIR Dermatol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e18091","DOI":"10.2196\/18091","article-title":"Artificial intelligence and its effect on dermatologists\u2019 accuracy in dermoscopic melanoma image classification: Web-based survey study","volume":"22","author":"Maron","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Marsden, H., Morgan, C., Austin, S., DeGiovanni, C., Venzi, M., Kemos, P., Greenhalgh, J., Mullarkey, D., and Palamaras, I. (2023). Effectiveness of an image-analyzing AI-based digital health technology to identify non-melanoma skin cancer and other skin lesions: Results of the DERM-003 study. Front. Med., 10.","DOI":"10.3389\/fmed.2023.1288521"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.jdin.2025.07.007","article-title":"Prospective evaluation of an AI-enabled elastic scattering spectroscopy device for triage of patient-identified skin lesions in dermatology clinics","volume":"23","author":"Jaklitsch","year":"2025","journal-title":"JAAD Int."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1038\/s41591-020-0942-0","article-title":"Human-computer collaboration for skin-cancer recognition","volume":"26","author":"Tschandl","year":"2020","journal-title":"Nat. Med."}],"container-title":["Medicina"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1648-9144\/61\/12\/2186\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T13:00:02Z","timestamp":1765371602000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1648-9144\/61\/12\/2186"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,10]]},"references-count":29,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["medicina61122186"],"URL":"https:\/\/doi.org\/10.3390\/medicina61122186","relation":{},"ISSN":["1648-9144"],"issn-type":[{"value":"1648-9144","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,10]]}}}