Diagnosis of skin melanoma using artificial intelligence using the computer vision program
https://doi.org/10.30629/0023-2149-2025-103-7-540-545
Abstract
A study was conducted on the effectiveness of the program for visual identification of skin melanoma “Melanoma Check” (a computer program for a smartphone), which showed high accuracy in determining the probability of a patient having skin melanoma. The diagnostic accuracy of the program was 88%, sensitivity — 77%, specificity — 91%, the proportion of false positive results — 9.2%, the proportion of false negative results — 23.1%. Due to the widespread use of smartphones, modern artificial intelligence technology makes it possible to early detect skin melanoma during the initial examination by a general practitioner and therapist, especially in medical organizations in regions with a shortage or absence of dermatovenerologists and oncologists.
About the Authors
A. I. LamotkinRussian Federation
Andrey I. Lamotkin — Assistant at the Department of Internal Medicine with courses in Family Medicine, Functional Diagnostics, Infectious Diseases, and Occupational Diseases at the Medical Faculty; specialist in the monitoring and analysis of activities of the federal project “Combating Oncological Diseases”
Moscow
D. I. Korabelnikov
Russian Federation
Daniil I. Korabelnikov — Candidate of Medical Sciences, Associate Professor, Head of the Department of Internal Medicine with courses in Family Medicine, Functional Diagnostics, Infectious Diseases, Occupational Diseases of the Faculty of Medicine, Rector
Moscow; Podolsk
I. A. Lamotkin
Russian Federation
Igor A. Lamotkin — Doctor of Medical Sciences, Professor, Head of the Skin and Venereology Department; Professor of the Department of Skin and Venereal Diseases with a cosmetology course at the Medical Institute of Continuing Education
Moscow
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Review
For citations:
Lamotkin A.I., Korabelnikov D.I., Lamotkin I.A. Diagnosis of skin melanoma using artificial intelligence using the computer vision program. Clinical Medicine (Russian Journal). 2025;103(7):540-545. (In Russ.) https://doi.org/10.30629/0023-2149-2025-103-7-540-545
































