Head-to-Head Evaluation

Artificial intelligence model outperforms clinicians in diagnosing pediatric ear infections

Close up of female doctor looking into otoscope held up to a female pediatric patient
Lordn / iStock / Getty Images Plus

An artificial intelligence model built by Harvard Medical School and Massachusetts Eye and Ear scientists was shown to be significantly more accurate than doctors at diagnosing pediatric ear infections in the first head-to-head evaluation of its kind, the research team working to develop the model for clinical use reported.

According to a new study published Aug. 16 in Otolaryngology–Head and Neck Surgery, the model, called OtoDX, was more than 95 percent accurate in diagnosing an ear infection in a set of 22 test images compared with 65 percent accuracy among a group of clinicians consisting of ENTs, pediatricians, and primary care doctors, who reviewed the same images.

Get more HMS news here

When tested in a data set of more than 600 inner ear images, the AI model had a diagnostic accuracy of more than 80 percent, representing a significant leap over the average accuracy of clinicians reported in medical literature.