**4.2 Diagnostics**

Both LLMs and ML have utilization within diagnostics. LLMs can serve as an adjunct to the patient evaluation process by suggesting rarer diagnoses and interventions that the physician may not have typically considered. These can be incorporated with the overall clinical picture as appropriate. The potential scope of which ML can be applied to diagnostics is largely divided between three categories: classification, detection, and segmentation. Classification involves algorithmic stratification of data inputs into categories (e.g., normal, abnormal). Detection entails visual localization of an area of interest (e.g., lesion). Segmentation implies outlining a target area using a precise, pixel-wise boundary [58]. The following categories will elucidate the various areas through which general ML and deep learning (DL) models have been applied to neurodiagnostics.
