Artificial Intelligence-Enabled Pharmacovigilance for Neuropsychiatric Therapies: Advancing U.S. Drug Safety Surveillance and Regulatory Decision-Making

Neuropsychiatric medications, including antidepressants, antipsychotics, mood stabilizers, anxiolytics, and newer agents such as esketamine, are widely prescribed, but their adverse effects are often subjective, delayed in onset, and difficult to separate from the illness being treated.Post-marketing surveillance in the United States relies on the FDA Adverse Event Reporting System (FAERS), MedWatch, the Sentinel System, and Risk Evaluation and Mitigation Strategies (REMS).This narrative review, reported according to the Scale for the Assessment of Narrative Review Articles (SANRA), examines how well these systems detect neuropsychiatric adverse events and what artificial intelligence (AI) can add.PubMed/MEDLINE, Embase, and Web of Science were searched for literature published since January 2000, supplemented by regulatory and institutional sources.Spontaneous reporting misses most adverse drug reactions, while stigma, diagnostic overshadowing, and uncertainty about whether a symptom reflects the drug or the disorder further reduce reporting of psychiatric effects.Disproportionality signals, such as those reported for dissociation and suicidal ideation with esketamine, need cautious interpretation because notoriety bias, solicited reporting under REMS, and confounding by indication can inflate them.Long-latency effects such as tardive dyskinesia and antipsychotic-induced metabolic change fall outside the observation windows of premarket trials and most spontaneous reports.Natural language processing (NLP) can retrieve adverse events documented in free-text psychiatric records with good accuracy, and large language models (LLMs) show early promise for structuring adverse event narratives, but most AI studies are retrospective proofs of concept, and models trained on biased data can reproduce existing inequities.AI is therefore best used to widen and prioritize signal detection, with causality assessment and regulatory decisions remaining with clinicians and regulators.Priorities include prospective evaluation of AI tools in live pharmacovigilance workflows, linkage of FAERS with electronic health record and claims data, point-ofcare reporting tools, and efforts to reduce the stigma that discourages reporting of psychiatric adverse effects.