Diagnostic accuracy of otitis media with and without a fictitious AI support among physicians in primary care and medical students

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Abstract
Otitis media (OM) in children is a common infection in primary care, contributing to a significant global health and economic burden. In high-income countries, diagnostic inaccuracy leads to over-diagnosis of acute OM (AOM) and over-prescribing of antibiotics, which may contribute to antibiotic resistance. To investigate the diagnostic accuracy and the influence of artificial intelligence (AI) in diagnosing OM among primary care physicians and medical students. A diagnostic accuracy study in which primary care physicians and medical students diagnosed AOM, OM with effusion (OME), and normal eardrums using 21 high-quality digital images, both without and with a fictitious AI support. We estimated the technological impact of the fictitious AI support. Overall diagnostic accuracy was 64% without, and 75% with AI support. The most experienced physicians reached 69% without, and 80% with AI; the least experienced 61% without, and 73% with AI; medical students reached 64% without, and 74% with AI. Accuracy for AOM was 77% without and 86% with AI, and for OME 46% without and 66% with AI. Mean diagnostic confidence increased significantly with AI support. The technological impact was 1.4. Automation bias was 1.2 overall, 0.9 for the most experienced and 1.2 for the least experienced physicians. We report modest diagnostic accuracy for OM among primary care physicians and medical students. The fictitious AI support system improved both accuracy and diagnostic confidence and reduced over-diagnosis. The most experienced physicians achieved the highest accuracy, the less experienced were more often misled by the fictitious AI.
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  • Vetenskapsrådet

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