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In view of the high mortality and the need for early diagnosis of melanoma, Harrington et al.(1) reviewed the studies on the diagnostic rules to stratify patients with suspected melanoma and concluded that the ABCD rule is more useful than the 7-point checklist. Despite the importance of this result, future evaluations of diagnostic methods should also include among the comparisons the diagnostic tools assisted by artificial intelligence. Computational analysis of dermatological images has shown great potential as a diagnostic tool for melanoma (2,3), and can contribute to reduce costs, increase access and the scope of the examination to regions without specialists, allowing early diagnosis by primary care physicians, mainly in remote areas, lacking specialists.
1. Harrington, E., Clyne, B., Wesseling, N., Sandhu, H., Armstrong, L., Bennett, H., & Fahey, T. (2017). Diagnosing malignant melanoma in ambulatory care: a systematic review of clinical prediction rules. BMJ Open, 7(3), e014096. http://doi.org/10.1136/bmjopen-2016-014096
2. Safran T, Viezel-Mathieu A, Corban J, Kanevsky A, Thibaudeau S, Kanevsky J. Machine learning and melanoma: The future of screening. J Am Acad Dermatol. 2018 Mar;78(3):620-621. doi: 10.1016/j.jaad.2017.09.055. Epub 2017 Oct 6. PubMed PMID: 28989109.
3. Jaworek-Korjakowska J, Kłeczek P. Automatic Classification of Specific Melanocytic Lesions Using Artificial Int...
3. Jaworek-Korjakowska J, Kłeczek P. Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence. Biomed Res Int.
2016;2016:8934242. doi: 10.1155/2016/8934242. Epub 2016 Jan 17. PubMed PMID: 26885520; PubMed Central PMCID: PMC4739011.