IPROC iproc Iproceedings 2369-6893 JMIR Publications Toronto, Canada v8i1e36894 10.2196/36894 Abstract Abstract Teledermatology and Artificial Intelligence Derrick Thomas Navarrete-Dechent Cristian MD 1
Melanoma and Skin Cancer Unit, Department of Dermatology Escuela de Medicina Pontificia Universidad Catolica de Chile Lira 40, Región Metropolitana Santiago, 8330077 Chile 56 2 2354 2000 ctnavarr@gmail.com
https://orcid.org/0000-0003-4040-3640 Melanoma and Skin Cancer Unit, Department of Dermatology Escuela de Medicina Pontificia Universidad Catolica de Chile Santiago Chile Corresponding Author: Cristian Navarrete-Dechent ctnavarr@gmail.com Jan-Dec 2022 9 2 2022 8 1 e36894 28 1 2022 28 1 2022 ©Cristian Navarrete-Dechent. Originally published in Iproceedings (https://www.iproc.org), 09.02.2022. 2022

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in Iproceedings, is properly cited. The complete bibliographic information, a link to the original publication on https://www.iproc.org/, as well as this copyright and license information must be included.

 Background

The use of artificial intelligence (AI) algorithms for the diagnosis of skin diseases has shown promise in experimental settings but has not yet been tested in real-life conditions. The COVID-19 pandemic led to a worldwide disruption of health systems, increasing the use of telemedicine. There is an opportunity to include AI algorithms in the teledermatology workflow.

 Objective

The aim of this study is to test the performance of and physicians’ preferences regarding an AI algorithm during the evaluation of patients via teledermatology.

 Methods

We performed a prospective study in 340 cases from 281 patients using patient-taken photos during teledermatology encounters. The photos were evaluated by an AI algorithm and the diagnosis was compared with the clinician’s diagnosis. Physicians also reported whether the AI algorithm was useful or not.

 Results

The balanced (in-distribution) 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=.049). 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. Algorithm performance was associated with patient skin type and image quality.

 Conclusions

AI algorithms appear to be a promising tool in the triage and evaluation of lesions in patient-taken photographs via telemedicine.

 Conflicts of Interest

None declared.

 teledermatology artificial intelligence diagnosis prospective augmented intelligence COVID-19