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

Abstract

Prior 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.