AI Model Versus Clinician Otoscopy in the Operative Setting for Otitis Media Diagnosis

Abstract

AbstractPrior work has demonstrated improved accuracy in otitis media diagnosis based on otoscopy using artificial intelligence (AI)‐based approaches compared to clinician evaluation. However, this difference in accuracy has not been shown in a setting resembling the point‐of‐care. In this study, we compare the diagnostic accuracy of a machine‐learning model to that of pediatricians using standard handheld otoscopes. We find that the model is more accurate than clinicians (90.6% vs 59.4%, P = .01). This is a step towards validation of AI‐based diagnosis under more real‐world conditions. With further validation, for example on different patient populations and in deployment, this technology could be a useful addition to the clinician's toolbox in accurately diagnosing otitis media.