Does generative AI mean the "end of history" for pharmacovigilance automation? towards a framework for the future of human-AI systems.

Abstract

Introduction: Advances in generative artificial intelligence (AI), particularly large language models (LLMs), have sparked discussions in automating pharmacovigilance (PV) workflows. It remains unclear whether these technological advancements fundamentally change the prior conclusions that full automation of Individual Case Safety Report (ICSR) processing is not feasible.

Methods: This perspective examines recent developments in AI for PV and introduces a conceptual framework of "computable PV," in which tasks are evaluated based on their computational tractability and suitability for automation.

Results: Routine, well-defined PV tasks, including completeness checks, detection of duplicated ICSRs, and structured information extraction, are increasingly amenable to automation. In contrast, complex activities such as case-level causality assessment remain difficult to formalize and continue to rely on expert judgment. The emergence of LLMs enables broader, cross-task capabilities compared to traditional task-specific, "small" models, but introduces challenges related to reliability, auditability, and governance. As a result, hybrid architecture combining large models, small models, and rule-based components is increasingly necessary.

Conclusion: Generative AI, as of today, does not signal full automation of PV but rather shifts toward hybrid human-AI systems. While AI can augment efficiency and support evidence synthesis, final decisions must remain under human oversight. Future PV systems should prioritize transparency, validation, and the integration of AI outputs into expert-driven decision-making.