Adoption of artificial intelligence in drug review across the lifecycle: Transformation of regulatory decision-making

초록

As regulatory authorities face increasingly complex data and resource constraints, artificial intelligence (AI) has emerged as a valuable tool for enhancing efficiency and evidence-based decision-making. This study aimed to examine global patterns of AI adoption in drug lifecycle, compare regulatory approaches, and identify key considerations for the safe and reliable integration of AI into regulatory practices. We conducted a comparative qualitative review of AI adoption in drug lifecycle across the United States, European Union, United Kingdom, and China by analyzing regulatory documents and policy publications; this was supplemented by literature review on AI applications across the drug lifecycle. AI adoption is advancing at different stages across jurisdictions, reflecting variations in institutional readiness, policy frameworks, and governance capacity. Regulatory authorities are primarily applying AI to support data-driven tasks with gradual movement toward AI-enabled regulatory workflows. Despite this progress, common challenges remain, including data bias, lack of explainability, unclear legal accountability, and regulatory inconsistency. Disparities in AI maturity and oversight frameworks highlight the need for international harmonization and structured human-AI governance. As AI increasingly supports drug review, strengthening regulators' technical understanding and critical capacity is essential to prevent overreliance on automated outputs and to ensure accountable and legitimate regulatory decision-making.

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