Recent Advances in Pharmacovigilance: Artificial Intelligence, Real?World Evidence, and Global Harmonization

Abstract

Pharmacovigilance (PV) has transitioned from reactive spontaneous reporting to a proactive, predictive science that integrates artificial intelligence (AI), real?world evidence (RWE), and global harmonization frameworks. Recent advances include machine learning algorithms for signal detection, natural language processing for case triage, and clinical decision support systems (CDSS) that flag drug-drug interactions at the point of care. Expanded surveillance for biologics, biosimilars, oncology agents, and pediatric medicines has intensified the need for tailored monitoring strategies and traceability. Regulatory bodies such as the EMA and FDA are driving harmonization through centralized reporting platforms and standardized coding systems (e.g., MedDRA), enabling cross?regional data sharing. Despite these innovations, challenges such as underreporting, heterogeneous data quality, algorithmic bias, and ethical concerns regarding transparency and privacy persist. Literature suggests hybrid human-AI models, risk?tiered CDSS, and globally harmonized infrastructures as future directions to balance innovation with ethical responsibility and patient safety.

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