AI-Powered Pharmacovigilance: Predictive Models for Adverse Drug Reaction Reporting

Abstract

Pharmacovigilance (PV) relies on the timely detection and reporting of adverse drug reactions (ADRs) to protect patients and refine benefit–risk profiles across the product life cycle. Traditional reporting pipelines—spontaneous reporting systems (SRS), periodic aggregate reviews, and manual case processing—are constrained by underreporting, delayed signal emergence, and the labor intensity of case triage. Recent advances in artificial intelligence (AI)—notably machine learning (ML), natural language processing (NLP), time-to-event modeling, and causal inference—offer a path to proactive safety surveillance that augments human judgment rather than replacing it. This manuscript proposes and details an end-to-end framework for AI-powered pharmacovigilance that predicts the likelihood of ADRs before or close to their onset and automatically prioritizes potential cases for structured reporting. We synthesize the literature on disproportionality analysis, supervised and semi-supervised learning on structured and unstructured data, and graph-based knowledge integration. We then delineate a transparent methodology emphasizing data governance, feature engineering aligned with MedDRA, model selection and calibration, imbalanced learning strategies, and rigorous internal and external validation. 

A prospective study protocol is presented covering cohort definitions, primary and secondary endpoints, analysis plans, fairness audits, and real-world implementation through “silent” deployment followed by human-in-the-loop adoption. In a simulated multi-center evaluation reflecting real-world reporting constraints, an ensemble model integrating gradient boosting for structured EHR data and transformer-based NLP for clinical narratives achieved AUROC 0.89, AUPRC 0.42 for rare outcomes, and reduced median time-to-signal by 21 days versus baseline workflows while preserving calibration and subgroup equity. The approach improved case prioritization efficiency (–28% reviewer time per valid case) and increased report completeness, without automating clinical decisions. We conclude that AI-enabled PV can responsibly accelerate signal detection and strengthen ADR reporting when implemented with rigorous validation, continuous monitoring for drift, fairness safeguards, and clear clinical governance.