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9th World Congress of Teledermatology, Imaging and AI for Skin Diseases - Abstracts (2021) (40)

Published on in Vol 8, No 1 (2022): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36894, first published .
Teledermatology and Artificial Intelligence

Teledermatology and Artificial Intelligence

Teledermatology and Artificial Intelligence

Authors of this article:

Cristian Navarrete-Dechent1 Author Orcid Image
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    Abstract

    Melanoma and Skin Cancer Unit, Department of Dermatology, Escuela de Medicina, Pontificia Universidad Catolica de Chile, Santiago, Chile

    Corresponding Author:

    Cristian Navarrete-Dechent, MD

    Melanoma and Skin Cancer Unit, Department of Dermatology

    Escuela de Medicina

    Pontificia Universidad Catolica de Chile

    Lira 40, Región Metropolitana

    Santiago, 8330077

    Chile

    Phone: 56 2 2354 2000

    Email: ctnavarr@gmail.com


    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.

    iproc 2022;8(1):e36894

    doi:10.2196/36894

    Keywords

    teledermatology; artificial intelligence; diagnosis; prospective; augmented intelligence; COVID-19 

    Edited by T Derrick; This is a non–peer-reviewed article. submitted 28.01.22; accepted 28.01.22; published 09.02.22

    Copyright

    ©Cristian Navarrete-Dechent. Originally published in Iproceedings (https://www.iproc.org), 09.02.2022.

    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.