Artificial intelligence (AI) has reached a critical juncture in its integration with healthcare and pharmaceutical developments. Regulatory agencies worldwide are developing frameworks to guide the responsible implementation of AI‐driven drug development and approval processes. The United States Food and Drug Administration (FDA) released its inaugural draft guidance in January 2025, specifically addressing the application of AI in regulatory decision‐making for pharmaceuticals and biological products. This review critically analyzes guidance, highlighting strengths such as the structured risk‐based credibility framework while identifying areas for refinement. Key recommendations include expanding the scope beyond regulatory decision‐making to include discovery and operational phases, strengthening bias mitigation strategies, establishing tiered explainability requirements, enhancing integration of real‐world evidence, and promoting global harmonization with other regulatory agencies, including EMA, NMPA, and PMDA. While the guidance represents a regulatory milestone, this review highlights the importance of continuous monitoring, equitable implementation across various sponsor sizes, and a patient‐centered approach. We examined terminological ambiguities, the importance of model explainability, risk stratification methodologies, the management of synthetic and real‐world data, and standards for cross‐functional collaboration. In addition, we analyzed the alignment of the guidelines with international initiatives. This review has several limitations: it relies primarily on publicly available sources and regulatory documents without empirical validation, it represents a single analyst’s interpretation of complex regulatory frameworks, and practical implementation challenges may differ from those presented in theoretical analyses. Despite these constraints, the study may stimulate meaningful engagement among stakeholders across academia, industry, and regulatory bodies to establish robust frameworks for AI implementation in drug development, while ensuring that safety, transparency, and equity remain central to AI’s transformative potential.
Sarfaraz K. Niazi (Thu,) studied this question.