Artificial intelligence in melanoma diagnosis: Ethical considerations and clinical implementation

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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Publication Details

Journal
Baylor University Medical Center Proceedings
Published
2025-05-05
DOI
https://doi.org/10.1080/08998280.2025.2489873
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
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article

Artificial intelligence in melanoma diagnosis: Ethical considerations and clinical implementation

Daniel P. Friedmann, Michelle Babb Tarbox, Kritin K. Verma, Ryan S. Koch et al.
Baylor University Medical Center Proceedings
Cutaneous Melanoma Detection and Management
article

Artificial intelligence in melanoma diagnosis: Ethical considerations and clinical implementation

Daniel P. Friedmann, Michelle Babb Tarbox, Kritin K. Verma, Ryan S. Koch, Kurt M. Grabow
article en

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.

Baylor University Medical Center ProceedingsVol. 38(4)
Texas Tech University (US), Dermatology Research Center (US), Texas A&M University (US), Texas Tech University Health Sciences Center (US)
Openalex Percentile: Top 85%
Cutaneous Melanoma Detection and Management
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Artificial intelligence in melanoma diagnosis: Ethical considerations and clinical implementation — Daniel P. Friedmann, Michelle Babb Tarbox, et al. · Baylor University Medical Center Proceedings (2025) | TGRS Research Map | TGRS