Evaluating the Accuracy of an Artificial Intelligence-Based Application for Diagnosing Temporomandibular Disorders - Haeseong Lee; Salma Awwad; Areeg Elmusrati; Chinmayee Patil; Sang Chung; Anette Vistoso; Amila Adili; Parish Sedghizadeh (Journal of the California Dental Association)
Background Temporomandibular disorders (TMD) present a diagnostic challenge, particularly for non-specialists, due to the complexity and variability of symptoms. This study evaluates the diagnostic accuracy of the AI-driven myTMJ© application (myTMJ©) by comparing its outputs to clinical diagnoses made by board-certified orofacial pain specialists at the Herman Ostrow School of Dentistry of USC. The app’s ability to identify the diagnosis most closely associated with a user’s chief complaint was assessed.Methods To test whether the observed accuracy of myTMJ differs from a hypothesized benchmark of 90%, a one-proportion z-test was conducted at a significance level of α = 0.05 (two-sided). Demographic characteristics, including age and gender, were summarized for the sample (n = 110). Continuous variables were reported as medians with interquartile ranges (IQR), while categorical variables were summarized as frequencies and percentages. The study included 110 patients, 30% male, with a median age of 34.0 years (IQR: 26.0–47.0).Results Of the 110 app-generated diagnoses, 105 (95.5%) were confirmed accurate by clinical diagnosis. The observed accuracy was significantly higher than the hypothesized 90%, as evidenced by a p-value of 0.036. The 95% confidence interval for the observed accuracy was [0.923, 0.997], further supporting the app’s high diagnostic performance.Conclusion These findings underscore myTMJ’s potential as a reliable tool for diagnosing TMD and orofacial pain. Notably, users answered questions under the supervision of orofacial pain residents, which may have contributed to high accuracy.Practical Implications Future studies should incorporate a broader sample and evaluate the app’s performance in an unsupervised setting to assess real-world diagnostic accuracy.