• C Muñoz-López, C Ramírez-Cornejo, M A Marchetti, S S Han, P Del Barrio-Díaz, A Jaque, P Uribe, D Majerson, M Curi, C Del Puerto, F Reyes-Baraona, R Meza-Romero, J Parra-Cares, P Araneda-Ortega, M Guzmán, R Millán-Apablaza, M Nuñez-Mora, K Liopyris, C Vera-Kellet, and C Navarrete-Dechent.
• Department of Dermatology, Escuela de Medicina, Pontificia Universidad Católica de Chile, Santiago, Chile.
• J Eur Acad Dermatol Venereol. 2021 Feb 1; 35 (2): 546-553.

BackgroundThe use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not been yet tested in real-life conditions.ObjectiveTo assess the diagnostic performance and potential clinical utility of a 174-multiclass AI algorithm in a real-life telemedicine setting.MethodsProspective, diagnostic accuracy study including consecutive patients who submitted images for teledermatology evaluation. The treating dermatologist chose a single image to upload to a web application during teleconsultation. A follow-up reader study including nine healthcare providers (3 dermatologists, 3 dermatology residents and 3 general practitioners) was performed.ResultsA total of 340 cases from 281 patients met study inclusion criteria. The mean (SD) age of patients was 33.7 (17.5) years; 63% (n = 177) were female. Exposure to the AI algorithm results was considered useful in 11.8% of visits (n = 40) and the teledermatologist correctly modified the real-time diagnosis in 0.6% (n = 2) of cases. The overall top-1 accuracy of the algorithm (41.2%) was lower than that of the dermatologists (60.1%), residents (57.8%) and general practitioners (49.3%) (all comparisons P < 0.05, in the reader study). When the analysis was limited to the diagnoses on which the algorithm had been explicitly trained, the balanced top-1 accuracy of the algorithm (47.6%) was comparable to the dermatologists (49.7%) and residents (47.7%) but superior to the general practitioners (39.7%; P = 0.049). Algorithm performance was associated with patient skin type and image quality.ConclusionsA 174-disease class AI algorithm appears to be a promising tool in the triage and evaluation of lesions with patient-taken photographs via telemedicine.© 2020 European Academy of Dermatology and Venereology.

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