Evaluations of artificial intelligence and machine learning algorithms in neurodiagnostics

Abstract

This article evaluates the ethical implications of utilizing artificial intelligence (AI) algorithms in neurological diagnostic examinations. Applications of AI technology have been utilized to aid in the determination of pharmacological dosages of gadolinium for brain lesion detection, localization of seizure foci, and the characterization of large vessel occlusion in ischemic stroke patients. Multiple subtypes of AI/machine learning (ML) algorithms are analyzed, as AI-assisted neurology utilizes supervised, unsupervised, artificial neural network (ANN), and deep neural network (DNN) learning models. As ANN and DNN analyses can be applied to data with an unknown clinical diagnosis, these algorithms are evaluated according to Bayesian statistical analyses. Bayesian neural network analyses are incorporated, as these algorithms indicate that the predictive accuracy and model performance are dependent upon accurate configurations of the model’s hyperparameters and neural inputs. Thus, mathematical evaluations of AI algorithms are comprehensively explored to examine their clinical utility, as underperformance of AI/ML models may have deleterious consequences that affect patient outcomes due to misdiagnosis and false-negative test results.