George A Zakhem1, Joseph W Fakhoury2, Catherine C Motosko1, Roger S Ho3. 1. Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York. 2. Wayne State University School of Medicine, Detroit, Michigan. 3. Ronald O. Perelman Department of Dermatology, New York University School of Medicine, New York, New York. Electronic address: Roger.Ho@nyumc.org.
Abstract
BACKGROUND: 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. OBJECTIVE: To characterize the evolution of AI in skin cancer assessment and characterize the involvement of dermatologists in developing these technologies. METHODS: 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. RESULTS: 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. LIMITATIONS: This review focused on models described in the medical literature and did not account for those described elsewhere. CONCLUSIONS: 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.
BACKGROUND: 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. OBJECTIVE: To characterize the evolution of AI in skin cancer assessment and characterize the involvement of dermatologists in developing these technologies. METHODS: 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. RESULTS: 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. LIMITATIONS: This review focused on models described in the medical literature and did not account for those described elsewhere. CONCLUSIONS: 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.
Authors: Ellen E Lee; John Torous; Munmun De Choudhury; Colin A Depp; Sarah A Graham; Ho-Cheol Kim; Martin P Paulus; John H Krystal; Dilip V Jeste Journal: Biol Psychiatry Cogn Neurosci Neuroimaging Date: 2021-02-08