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Artificial intelligence algorithm for real-time diagnostic assist in orofacial pain.

G. Clark,Anette Vistoso Monreal,Nicolas Veas,Gerald E Loeb

Published 2025 in The Journal of the American Dental Association (1939)

ABSTRACT

BACKGROUND Misdiagnosis is prevalent in clinical practice due to incomplete and sometimes inconsistent data, which generate errors in the traditional diagnostic process. Orofacial pain, with its wide range of conditions, poses a considerable diagnostic challenge, particularly for inexperienced clinicians. CASE DESCRIPTION A structured, machine learning-compatible note-taking system was used to document clinical history and examination features from 1,020 patients at an orofacial pain clinic. A naïve Bayesian inference algorithm was used to compute and display the probability of various diagnoses as data were added to the medical record during a clinical encounter. Its accuracy compared favorably with 5 machine learning algorithms for 5 new cases of each of 10 diagnoses varying in their prevalence in the database. PRACTICAL IMPLICATIONS The authors speculated that the key to achieving reasonable concordance was the highly structured electronic medical record, which included disease-defining or unique features of most diagnoses. Extension of these methods to broader clinical domains will require similar attention.

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