Kritin K Verma, Kurt M Grabow, Ryan S Koch, Daniel P Friedmann, Michelle B Tarbox
{"title":"Artificial intelligence in melanoma diagnosis: ethical considerations and clinical implementation.","authors":"Kritin K Verma, Kurt M Grabow, Ryan S Koch, Daniel P Friedmann, Michelle B Tarbox","doi":"10.1080/08998280.2025.2489873","DOIUrl":null,"url":null,"abstract":"<p><p>The use of artificial intelligence (AI) in dermatology, particularly for the diagnosis of melanoma, has demonstrated potential in improving early detection of cancer. Current AI-based systems, such as DermaSensor and Nevisense, have shown high sensitivity. In addition, open-source models like All Data Are Ext (ADAE) continue to show promise. Ethical, practical, and privacy concerns remain despite these advancements. Key challenges with these models include maintaining transparency with patients, ensuring privacy of patient data, and addressing discrepancies between AI and clinical determinations. Additional research, regulatory guidance, and open conversations are necessary to realize AI's full potential in the field of dermatology while preserving patient trust.</p>","PeriodicalId":8828,"journal":{"name":"Baylor University Medical Center Proceedings","volume":"38 4","pages":"577-578"},"PeriodicalIF":0.0000,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12184156/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Baylor University Medical Center Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/08998280.2025.2489873","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
The use of artificial intelligence (AI) in dermatology, particularly for the diagnosis of melanoma, has demonstrated potential in improving early detection of cancer. Current AI-based systems, such as DermaSensor and Nevisense, have shown high sensitivity. In addition, open-source models like All Data Are Ext (ADAE) continue to show promise. Ethical, practical, and privacy concerns remain despite these advancements. Key challenges with these models include maintaining transparency with patients, ensuring privacy of patient data, and addressing discrepancies between AI and clinical determinations. Additional research, regulatory guidance, and open conversations are necessary to realize AI's full potential in the field of dermatology while preserving patient trust.
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