Risk Management in AI-Assisted Clinical Trials: Regulatory and Ethical Dimensions

Abstract

The integration of artificial intelligence (AI) models into clinical trials has accelerated significantly over the past decade, offering substantial potential to optimise patient recruitment, endpoint analysis, and pharmacovigilance. However, this integration also
introduces a complex web of regulatory and ethical risks that must be systematically managed. This paper examined the principal risk categories associated with AI deployment in clinical research, with particular focus on algorithmic bias, the erosion of informed consent through opaque decision-making, and the evolving regulatory landscape shaped by the EU AI Act (Regulation EU 2024/1689), the European Medicines Agency (EMA) Reflection Paper on AI, and the U.S. Food and Drug Administration (FDA)
draft guidance on AI in drug development. A structured review of peer-reviewed literature and regulatory documents was conducted. The findings indicated that, while regulatory convergence between the FDA and EMA is advancing, significant gaps persist in practice concerning transparency, explainability, and equitable participant selection. The paper proposed a multi-layered risk governance framework that integrates lifecycle monitoring, ethics review, and human oversight as foundational components of responsible AI use in clinical trials.