Artificial Intelligence in Pharmacovigilance

A lot has been said about artificial intelligence (AI) and pharmacovigilance (PV), but for seasoned professionals we should have seen this coming a long time ago. The traditional way PV activities have been conducted relied heavily on manual, resource‑intensive processes. Case processing involved receiving and validating adverse event reports, entering them into databases, and ensuring regulatory submissions were complete. Narrative writing required medical reviewers to craft clear, regulatory‑compliant descriptions of patient experiences. Adverse event (AE) coding depended on accurate Medical Dictionary for Regulatory Activities (MedDRA) term selection, often a repetitive and error‑prone task. Case submissions demanded careful formatting and transmission of individual case safety reports (ICSRs). Aggregate reporting meant compiling periodic safety update reports (PSURs) and development safety update reports (DSURs) from large datasets. Signal detection traditionally relied on manual statistical reviews and expert judgment. Finally, quality and compliance oversight required extensive audits, reconciliations, and documentation to meet global regulatory standards.

Today, AI tools are reshaping each of these activities. Case processing can be streamlined with natural language processing (NLP) and automated intake systems that classify and prioritize reports. Narrative writing benefits from AI‑driven text generation that produces medically coherent drafts, reducing reviewer workload. AE coding is accelerated by machine learning models trained on MedDRA, improving consistency and reducing human error. Case submissions can be automated through intelligent workflow systems that ensure compliance with ICH E2B(R3) standards. Aggregate reporting is enhanced by AI‑powered analytics that synthesize large volumes of safety data into structured outputs. Signal detection has been revolutionized by machine learning algorithms capable of scanning real‑world data, electronic health records, and social media to identify emerging safety concerns faster than traditional disproportionality analyses. Finally, quality and compliance monitoring can leverage AI dashboards that flag anomalies, track metrics, and support continuous inspection readiness.

The promise of AI in pharmacovigilance is clear: efficiency, scalability, and potentially earlier detection of risks. Yet, regulators must define requirements such as validation procedures, transparency standards, and minimum safeguards to ensure these tools do not compromise patient safety. Automated systems must be held to the same rigor as traditional methods, with clear expectations for manufacturers on how AI models are trained, validated, and monitored. Only then can the industry fully harness AI’s potential while maintaining trust in the safety of medicines.

References

• DIA Global Forum. Artificial Intelligence and Machine Learning in Pharmacovigilance. DIA, 2025.

• CCRPS. Pharmacovigilance Case Processing: Definitive Expert Guide. CCRPS, 2025.

• LS Academy. Narrative Writing in Pharmacovigilance. LS Academy, 2025.

• Arithmos. Case Processing in Pharmacovigilance Operations. Arithmos, 2022.

• Freyr Solutions. Traditional vs AI Approaches in Pharmacovigilance Detection. Freyr, 2025.

• Warner J, Jardim A, Albera C. Artificial Intelligence: Applications in Pharmacovigilance Signal Management. Pharmaceutical Medicine. Springer, 2025.

• IntuitionLabs. AI Applications in Pharmacovigilance and Drug Safety. IntuitionLabs, 2025.

• Uppsala Reports. Artificial Intelligence in Pharmacovigilance: Harnessing Potential, Navigating Risks. UMC, 2024.

Assessed and Endorsed by the MedReport Medical Review Board