Characterizing the role of dermatologists in developing artificial intelligence for assessment of skin cancer

• Published in: Journal of the American Academy of Dermatology Vol. 85; no. 6; pp. 1544 - 1556
• Main Authors: Zakhem, George A., Fakhoury, Joseph W., Motosko, Catherine C., Ho, Roger S.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01-12-2021
• Subjects: Artificial Intelligence; computer vision; Dermatologists; Dermoscopy; Humans; machine learning; melanoma; Melanoma - diagnosis; nevi; pigmented lesions; skin cancer screening; Skin Neoplasms - diagnosis; skin cancer screening; computer vision; AI; melanoma; nevi; machine learning; artificial intelligence; pigmented lesions
• ISSN: 0190-9622; 1097-6787
• DOI: 10.1016/j.jaad.2020.01.028

The use of artificial intelligence (AI) for skin cancer assessment has been an emerging topic in dermatology. Leadership of dermatologists is necessary in defining how these technologies fit into clinical practice.
To characterize the evolution of AI in skin cancer assessment and characterize the involvement of dermatologists in developing these technologies.
An electronic literature search was performed using PubMed by searching machine learning or artificial intelligence combined with skin cancer or melanoma. Articles were included if they used AI for screening and diagnosis of skin cancer using data sets consisting of dermoscopic images or photographs of gross lesions.
Fifty-one articles were included, and 41% of these had dermatologists included as authors. Articles that included dermatologists described algorithms built with more images versus articles that did not include dermatologists (mean, 12,111 vs 660 images, respectively). In terms of underlying technology, AI used for skin cancer assessment has followed trends in the field of image recognition.
This review focused on models described in the medical literature and did not account for those described elsewhere.
Greater involvement of dermatologists is needed in thinking through issues in data collection, data set biases, and applications of technology. Dermatologists can provide access to large, diverse data sets that are increasingly important for building these models.