Artificial intelligence in epilepsy diagnosis: Clinical readiness, failure modes, and standards for Implementation

Epilepsy diagnosis is not a single classification task but a sequence of clinically consequential decisions: whether a paroxysmal event is epileptic, whether the patient meets the definition of epilepsy, how seizures and epilepsy should be classified, and what etiology or epileptogenic network explains the disorder. Artificial intelligence (AI) can support these decisions through natural language processing, conversational systems, computer vision, wearable sensors, electroencephalography (EEG), magnetic resonance imaging (MRI), genetics, and multimodal models. The evidence is uneven. Automated EEG interpretation is the most mature domain, with large expert-annotated datasets and external validation, but false-positive epileptiform activity and automation bias remain important safety concerns. Outcome-anchored MRI postprocessing, molecular imaging, and PET/MRI integration can reveal focal cortical dysplasia, temporal-limbic abnormalities, or metabolic localization hypotheses missed by visual review, whereas video and wearable systems extend observation into the home but perform best for conspicuous motor events. Language models can structure histories and records but remain vulnerable to missing context, hallucination, and inherited documentation bias. This Full Review organizes the field around four diagnostic questions and evaluates each modality against the reference standard appropriate to its intended use. The decisive transition is from algorithmic performance to clinical utility: external validation, calibration, transparent uncertainty, human-AI interaction studies, regulatory status and intended-use labeling, workflow integration, equitable performance, prospective effects on diagnostic delay and misdiagnosis, and continuous monitoring after deployment. AI should support accountable clinician-patient decisions rather than function as an autonomous epileptologist.