A Novel Bayesian General Medical Diagnostic Assistant Achieves Superior Accuracy With Sparse History - Jones, Alicia M.; Jones, Daniel R. (Frontiers in Artificial Intelligence, 2022)
Online AI symptom checkers and diagnostic assistants (DAs) have tremendous potential to reduce misdiagnosis and cost, while increasing the quality, convenience, and availability of healthcare, but only if they can perform with high accuracy. We introduce a novel Bayesian DA designed to improve diagnostic accuracy by addressing key weaknesses of Bayesian Network implementations for clinical diagnosis. We compare the performance of our prototype DA (MidasMed) to that of physicians and six other publicly accessible DAs (Ada, Babylon, Buoy, Isabel, Symptomate, and WebMD) using a set of 30 publicly available case vignettes, and using only sparse history (no exam findings or tests). Our results demonstrate superior performance for MidasMed with the correct diagnosis being the top ranked disorder in 93.3% of cases, and in the top 3 in 96.7% of cases.