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Hay RJ, Johns NE, Williams HC, Bolliger IW, Dellavalle RP, Margolis DJ, Marks R, Naldi L, Weinstock MA, Wulf SK, Michaud C, J L Murray C, Naghavi M. The global burden of skin disease in 2010: An analysis of the prevalence and impact of skin conditions. J Invest Dermatol. 2014; 134:1527-1534.","DOI":"10.1038\/jid.2013.446"},{"key":"2","doi-asserted-by":"crossref","unstructured":"2. Karimkhani C, Dellavalle RP, Coffeng LE, Flohr C, Hay RJ, Langan SM, Nsoesie EO, Ferrari AJ, Erskine HE, Silverberg JI, Vos T, Naghavi M. Global skin disease morbidity and mortality: An update from the Global Burden of Disease Study 2013. JAMA Dermatol. 2017; 153:406-412.","DOI":"10.1001\/jamadermatol.2016.5538"},{"key":"3","doi-asserted-by":"crossref","unstructured":"3. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542:115-118.","DOI":"10.1038\/nature21056"},{"key":"4","doi-asserted-by":"crossref","unstructured":"4. Haenssle HA, Fink C, Schneiderbauer R, <i>et al<\/i>. Man against machine: Diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol. 2018; 29:1836-1842.","DOI":"10.1093\/annonc\/mdy520"},{"key":"5","doi-asserted-by":"crossref","unstructured":"5. Tschandl P, Rinner C, Apalla Z, <i>et al<\/i>. Human-computer collaboration for skin cancer recognition. Nat Med. 2020; 26:1229-1234.","DOI":"10.1038\/s41591-020-0942-0"},{"key":"6","doi-asserted-by":"crossref","unstructured":"6. Liu Y, Jain A, Eng C, <i>et al<\/i>. A deep learning system for differential diagnosis of skin diseases. Nat Med. 2020; 26:900-908.","DOI":"10.1038\/s41591-020-0842-3"},{"key":"7","doi-asserted-by":"crossref","unstructured":"7. Han SS, Park I, Eun Chang S, Lim W, Kim MS, Park GH, Chae JB, Huh CH, Na JI. Augmented intelligence dermatology: Deep neural networks empower medical professionals in diagnosing skin cancer and predicting treatment options for 134 skin disorders. J Invest Dermatol. 2020; 140:1753-1761.","DOI":"10.1016\/j.jid.2020.01.019"},{"key":"8","doi-asserted-by":"crossref","unstructured":"8. Tschandl P, Rosendahl C, Kittler H. The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data. 2018; 5:180161.","DOI":"10.1038\/sdata.2018.161"},{"key":"9","doi-asserted-by":"crossref","unstructured":"9. Marchetti MA, Codella NCF, Dusza SW, <i>et al<\/i>. Results of the 2016 International Skin Imaging Collaboration International Symposium on Biomedical Imaging challenge: Comparison of the accuracy of computer algorithms to dermatologists for the diagnosis of melanoma from dermoscopic images. J Am Acad Dermatol. 2018; 78:270-277.e1.","DOI":"10.1016\/j.jaad.2017.08.016"},{"key":"10","doi-asserted-by":"crossref","unstructured":"10. Moor M, Banerjee O, Abad ZSH, <i>et al<\/i>. Foundation models for generalist medical artificial intelligence. Nature. 2023; 616:259-265.","DOI":"10.1038\/s41586-023-05881-4"},{"key":"11","doi-asserted-by":"crossref","unstructured":"11. Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med. 2023; 388:1201-1208.","DOI":"10.1056\/NEJMra2302038"},{"key":"12","doi-asserted-by":"crossref","unstructured":"12. Rajpurkar P, Chen E, Banerjee O, Topol EJ. AI in health and medicine. Nat Med. 2022; 28:31-38.","DOI":"10.1038\/s41591-021-01614-0"},{"key":"13","doi-asserted-by":"crossref","unstructured":"13. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019; 25:44-56.","DOI":"10.1038\/s41591-018-0300-7"},{"key":"14","doi-asserted-by":"crossref","unstructured":"14. Lu MY, Chen B, Williamson DFK, <i>et al<\/i>. A multimodal generative AI copilot for human pathology. Nature. 2024; 634:466-473.","DOI":"10.1038\/s41586-024-07618-3"},{"key":"15","doi-asserted-by":"crossref","unstructured":"15. Yan S, Yu Z, Primiero C, <i>et al<\/i>. A multimodal vision foundation model for clinical dermatology. Nat Med. 2025; 31:2691-2702.","DOI":"10.1038\/s41591-025-03747-y"},{"key":"16","doi-asserted-by":"crossref","unstructured":"16. Daneshjou R, Vodrahalli K, Novoa RA, <i>et al<\/i>. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci Adv. 2022; 8:eabq6147.","DOI":"10.1126\/sciadv.abq6147"},{"key":"17","doi-asserted-by":"crossref","unstructured":"17. Adamson AS, Smith A. Machine learning and health care disparities in dermatology. JAMA Dermatol. 2018; 154:1247-1248.","DOI":"10.1001\/jamadermatol.2018.2348"},{"key":"18","doi-asserted-by":"crossref","unstructured":"18. Choy SP, Kim BJ, Paolino A, Tan WR, Lim SML, Seo J, Tan SP, Francis L, Tsakok T, Simpson M, Barker JNWN, Lynch MD, Corbett MS, Smith CH, Mahil SK. Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease. NPJ Digit Med. 2023; 6:180.","DOI":"10.1038\/s41746-023-00914-8"},{"key":"19","doi-asserted-by":"crossref","unstructured":"19. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019; 17:195.","DOI":"10.1186\/s12916-019-1426-2"},{"key":"20","doi-asserted-by":"crossref","unstructured":"20. Widiawaty A, Indriatmi W, Jatmiko W, Novianto E, Kekalih A, Gunawan H, Palar PS, Rachmadi MF, Dermawan S, Malahayati TL, Ramadhan AW. Multimodal machine learning approach for diagnosing atopic dermatitis. F1000Res. 2025; 14:952.","DOI":"10.12688\/f1000research.169102.1"},{"key":"21","doi-asserted-by":"crossref","unstructured":"21. Tschandl P, Rosendahl C, Akay BN, <i>et al<\/i>. Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks. JAMA Dermatol. 2019; 155:58-65.","DOI":"10.1001\/jamadermatol.2018.4378"},{"key":"22","doi-asserted-by":"crossref","unstructured":"22. Brinker TJ, Hekler A, Enk AH, Klode J, Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D, Holland-Letz T, Utikal JS, von Kalle C; Collaborators. Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task. Eur J Cancer. 2019; 113:47-54.","DOI":"10.1016\/j.ejca.2019.04.001"},{"key":"23","doi-asserted-by":"crossref","unstructured":"23. Goyal M, Knackstedt T, Yan S, Hassanpour S. Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities. Comput Biol Med. 2020; 127:104065.","DOI":"10.1016\/j.compbiomed.2020.104065"},{"key":"24","doi-asserted-by":"crossref","unstructured":"24. Pacheco AGC, Lima GR, Salom\u00e3o AS, <i>et al<\/i>. PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones. Data Brief. 2020; 32:106221.","DOI":"10.1016\/j.dib.2020.106221"},{"key":"25","doi-asserted-by":"crossref","unstructured":"25. Freeman K, Dinnes J, Chuchu N, Takwoingi Y, Bayliss SE, Matin RN, Jain A, Walter FM, Williams HC, Deeks JJ. Algorithm based smartphone apps to assess risk of skin cancer in adults: Systematic review of diagnostic accuracy studies. BMJ. 2020; 368:m127.","DOI":"10.1136\/bmj.m127"},{"key":"26","doi-asserted-by":"crossref","unstructured":"26. Monheit G, Cognetta AB, Ferris L, Rabinovitz H, Gross K, Martini M, Grichnik JM, Mihm M, Prieto VG, Googe P, King R, Toledano A, Kabelev N, Wojton M, Gutkowicz-Krusin D. The performance of MelaFind: A prospective multicenter study. Arch Dermatol. 2011; 147:188-194.","DOI":"10.1001\/archdermatol.2010.302"},{"key":"27","doi-asserted-by":"crossref","unstructured":"27. Venkatesh KP, Kvedar JC. Learnings from the first AI-enabled skin cancer device for primary care authorized by FDA. NPJ Digit Med. 2024; 7:167.","DOI":"10.1038\/s41746-024-01161-1"},{"key":"28","doi-asserted-by":"crossref","unstructured":"28. Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): A comparative analysis. Lancet Digit Health. 2021; 3:e195-e203.","DOI":"10.1016\/S2589-7500(20)30292-2"},{"key":"29","doi-asserted-by":"crossref","unstructured":"29. Benjamens S, Dhunnoo P, Mesko B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: An online database. 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