In evaluating if body part information may increase top-5 accuracy, we saw top-5 accuracy for one condition increase from 67% to 97%.Ĭonclusions: Overall, we concluded that including body part information to down-select possible disease matches substantially increased the overall differential diagnosis accuracy for body region–specific conditions. Additionally, we trained the AI model across 54 conditions and achieved 74.3% top-5 accuracy across common conditions and 79.2% top-5 accuracy across urgent conditions. Results: Overall, we trained an AI model to identify 26 classes on par with the accuracy level of a dermatologist, who is, on average, 75% top-3 accurate across 26 conditions. Additionally, we further tested narrowing the differential diagnosis by adding body part information to identify how this impacts top-5 accuracy for one condition. The number of images within each disease or class ranged from 76 to 5505. ![]() Methods: We trained our AI models with approximately 50,000 total photos. Objective: The aim of this study was to build artificial intelligence (AI) classifiers across 26 and 54 common and urgent adult rashes that present in a primary care setting. Published studies have shown that the diagnostic accuracy of a PCP or general practitioner is close to 50%. Due to the scarcity of dermatologists, 2 in 3 cases are seen by primary care physicians (PCPs), who have lower diagnostic accuracy. JMIR Bioinformatics and Biotechnology 12 articlesĮmail: Skin diseases affect 2.3 billion people globally.JMIR Biomedical Engineering 58 articles.JMIR Perioperative Medicine 61 articles.Journal of Participatory Medicine 68 articles. ![]()
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