aceDiagnostic Accuracy of Artificial Intelligence versus Clinical Diagnosis in Detecting Acute Otitis Media: A Systematic Review and Meta-analysis
Abstract Acute otitis media (AOM) represents a prevalent infection in children globally, leading to considerable morbidity and imposing a substantial burden on healthcare systems. Prompt and precise diagnosis is critical to reducing complications, such as tympanic membrane perforation, hearing impairment, and excessive antibiotic prescribing. Advances in artificial intelligence (AI), particularly deep learning, have created opportunities to enhance diagnostic accuracy and broaden access to care, especially in lo…
Across nine studies, AI systems detected acute otitis media with pooled sensitivity of 91% and specificity of 99%, matching or beating general practitioners and pediatricians.
The pooled sensitivity estimate has a wide confidence interval (77%–97%), and the evidence base is only nine studies, so the precision and generalizability of the performance estimate are uncertain.
Evidence
- Peer-reviewedSaudi Journal of Otorhinolaryngology Head and Neck Surgery2026-10-22
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Truvace Impact Record TRV-2026-2182, v1: “Diagnostic Accuracy of Artificial Intelligence versus Clinical Diagnosis in Detecting Acute Otitis Media: A Systematic Review and Meta-analysis.” Truvace, 2026-10-22. /record/TRV-2026-2182 (accessed at citation time). sha256 2a268a7830fbee73…
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