AI Pharmacovigilance Workflow for Detecting Neurological Adverse Events from Reports, Notes, and Literature Download PDF

Neurological adverse drug events are clinically consequential and may be missed when surveillance depends on a single source of evidence. Under-reporting, delayed recognition, and fragmented documentation make these events especially challenging for conventional pharmacovigilance workflows. Potential neurological safety signals may appear separately in spontaneous reports, electronic health record notes, and published biomedical literature. A unified AI workflow is needed to connect these evidence streams in a timely, traceable, and reviewer-ready manner. This article proposes an AI pharmacovigilance workflow that ingests spontaneous reports, clinical notes, and biomedical literature, then applies transformer-based NLP to identify neurological adverse event mentions. The extracted evidence is fused into a dynamic risk score with source attribution and reviewer-facing explanations. The workflow includes data ingestion and harmonization, multi-source NLP extraction, signal fusion, disproportionality analysis, confounder-aware alerting, and a human-review dashboard. Each module is designed to preserve links between computational outputs and the original source evidence. The proposed workflow would be expected to support earlier recognition of rare neurological safety concerns by cross-validating signals across heterogeneous data streams. It could reduce unsupported alerts by distinguishing consistent multi-source evidence from isolated or ambiguous mentions. A holistic, AI-driven surveillance system could transform pharmacovigilance from a fragmented, reactive process into an integrated, proactive safety intelligence function. Its value would depend on transparent evidence handling, expert oversight, and careful validation in operational settings.