1. Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices
- Author
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Vishakha Gupta, Rory Sayres, Ayush Jain, David H. Way, Lily Peng, Yun Liu, Karen DeSalvo, Jay David Hartford, Yuan Liu, Peggy Bui, Clara H. Eng, Greg S. Corrado, Carter Dunn, Dale R. Webster, David Coz, Guilherme de Oliveira Marinho, Yi Gao, Susan Jen Huang, Kunal Nagpal, and Kimberly Kanada
- Subjects
Teledermatology ,Telemedicine ,Randomization ,Referral ,business.industry ,education ,Guinea Pigs ,MEDLINE ,General Medicine ,Mobile Applications ,Search Engine ,Interquartile range ,Artificial Intelligence ,Medicine ,Animals ,Humans ,Medical history ,Artificial intelligence ,Medical diagnosis ,business ,health care economics and organizations ,Original Investigation - Abstract
IMPORTANCE: Most dermatologic cases are initially evaluated by nondermatologists such as primary care physicians (PCPs) or nurse practitioners (NPs). OBJECTIVE: To evaluate an artificial intelligence (AI)–based tool that assists with diagnoses of dermatologic conditions. DESIGN, SETTING, AND PARTICIPANTS: This multiple-reader, multiple-case diagnostic study developed an AI-based tool and evaluated its utility. Primary care physicians and NPs retrospectively reviewed an enriched set of cases representing 120 different skin conditions. Randomization was used to ensure each clinician reviewed each case either with or without AI assistance; each clinician alternated between batches of 50 cases in each modality. The reviews occurred from February 21 to April 28, 2020. Data were analyzed from May 26, 2020, to January 27, 2021. EXPOSURES: An AI-based assistive tool for interpreting clinical images and associated medical history. MAIN OUTCOMES AND MEASURES: The primary analysis evaluated agreement with reference diagnoses provided by a panel of 3 dermatologists for PCPs and NPs. Secondary analyses included diagnostic accuracy for biopsy-confirmed cases, biopsy and referral rates, review time, and diagnostic confidence. RESULTS: Forty board-certified clinicians, including 20 PCPs (14 women [70.0%]; mean experience, 11.3 [range, 2-32] years) and 20 NPs (18 women [90.0%]; mean experience, 13.1 [range, 2-34] years) reviewed 1048 retrospective cases (672 female [64.2%]; median age, 43 [interquartile range, 30-56] years; 41 920 total reviews) from a teledermatology practice serving 11 sites and provided 0 to 5 differential diagnoses per case (mean [SD], 1.6 [0.7]). The PCPs were located across 12 states, and the NPs practiced in primary care without physician supervision across 9 states. The NPs had a mean of 13.1 (range, 2-34) years of experience and practiced in primary care without physician supervision across 9 states. Artificial intelligence assistance was significantly associated with higher agreement with reference diagnoses. For PCPs, the increase in diagnostic agreement was 10% (95% CI, 8%-11%; P
- Published
- 2021