Nagar Ankit, Gobburu Joga, Chakravarty Aloka
University of Maryland Baltimore, Rm S41020 N Pine St, Baltimore, MD 21201, USA.
Department of Practice, Science, and Health Outcomes Research, School of Pharmacy, University of Maryland, Baltimore, MD, USA.
Ther Adv Drug Saf. 2025 Jul 31;16:20420986251361435. doi: 10.1177/20420986251361435. eCollection 2025.
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.
人工智能（AI）已迅速从药物警戒（PV）中的实验性应用发展到被考虑用于常规用途。本综述批判性地审视了人工智能在彻底改变药物安全监测方面的潜力，重点关注实际实施挑战，如确保人工智能性能的一致性和透明度、减少多种偏差来源以及解决可解释性问题。它强调了从实验性使用到药物警戒中常规、可扩展能力的转变。它研究了人工智能在特定应用中的证据基础、增强可采取行动见解的能力，以及组织如何在多人工智能系统环境中防范意外后果。随着人工智能在药物警戒中从理论走向实践，这些考量至关重要。
