Integration of artificial intelligence into pharmacovigilance: A systematic review

Recent advances in machine learning (ML) have sparked the rise of artificial intelligence (AI) in pharmacovigilance (PV). Several studies suggest these technologies could address key PV challenges, including underreporting, manual case processing, and delayed signal detection.

This systematic review aimed to map AI/ML applications across post-marketing PV functions, characterize methodological and geographic trends, and identify evidence gaps in external validation and implementation readiness.

Following PRISMA 2020 and a Population, Concept, Context (PCC) framework, eight bibliographic databases were searched from January 2021 through October 2024, supplemented by grey literature and reference-list screening. Sixty-seven studies were included. Two reviewers independently screened, extracted, and appraised studies, with a third reviewer verifying extraction. Methodological quality was assessed using APPRAISE-AI, and certainty of evidence was considered using GRADE principles.

Safety-signal detection was the most common application (50.5%), followed by adverse effect prediction (21%), case-processing efficiency (15%), drug-drug interaction detection (7.5%), and high-risk patient identification (6%). XGBoost and ensemble methods dominated structured-data applications, while BERT-family transformers prevailed for unstructured text. However, external validation was the weakest APPRAISE-AI domain (mean score 52%), and prospective implementation studies were rare.

Artificial intelligence demonstrates substantial potential to transform pharmacovigilance from reactive to proactive systems. However, current evidence is largely retrospective, with limited external validation and real-world implementation. Although BERT and XGBoost demonstrate promising performance in signal detection, heterogeneous methodologies limit conclusions regarding generalizability and clinical impact. Responsible integration into safety-monitoring workflows requires rigorous independent validation, prospective human-in-the-loop evaluation, standardized performance metrics, and geographically representative datasets. Addressing data privacy, algorithmic fairness, human oversight, and regulatory requirements will also be crucial. Future research should therefore focus on robust validation and real-world implementation to ensure that AI translates into reliable, clinically meaningful improvements in patient safety.