Artificial intelligence (AI) has rapidly evolved from experimental applications in pharmacovigilance (PV) to being considered for routine use. This review critically examines AI's potential to revolutionize drug safety monitoring, focusing on practical implementation challenges such as ensuring AI's consistent and transparent performance, reducing multiple sources of bias, and addressing interpretability issues. It emphasizes the transition from experimental use to a routine, scalable capability within PV. It examines AI's evidence base in specific applications, its ability to enhance actionable insights, and how organizations can safeguard against unintended consequences in multi-AI system environments. These considerations are vital as AI moves from theory to practice in PV.
Nagar et al. (2025) studied this question.