Abstract
Artificial intelligence (AI) is increasingly being integrated into clinical research across multiple stages of the trial lifecycle, including protocol development, patient recruitment and stratification, data management, statistical analysis, and safety surveillance. By enabling rapid processing of large and complex datasets, AI-driven tools offer significant advantages in improving operational efficiency, enhancing predictive accuracy, reducing trial timelines and supporting evidence-based decision-making. These capabilities position AI as a transformative technology with the potential to address long-standing challenges in clinical research, such as recruitment inefficiencies, high costs, and variability in data quality.
Despite these benefits, the implementation of AI in clinical research raises substantial regulatory and ethical challenges that must be carefully addressed. Considerable variability exists in global regulatory frameworks governing AI use, creating uncertainty for sponsors and investigators conducting multinational trials. Concerns related to data privacy, cyber security, algorithmic bias, lack of transparency, unclear accountability and limitations of traditional informed consent processes present significant barriers to the responsible adoption of AI technologies. Regulatory authorities, including the U.S. Food and Drug Administration (FDA), the European Union (EU), the World Health Organization (WHO) and various national agencies, have introduced guidance and policy frameworks aimed at mitigating AI-related risks; however, inconsistencies and limited harmonization across jurisdictions continue to impede uniform implementation.Ethical frameworks increasingly emphasize the principles of fairness, transparency, human oversight, patient autonomy, and the protection of sensitive health data to ensure trust and accountability in AI-enabled research. This review provides a comprehensive global overview of the evolving regulatory approaches governing the use of AI in clinical research and critically examines the ethical challenges associated with its application. Furthermore, practical recommendations are proposed for researchers, sponsors, ethics committees, and regulators to promote the safe, transparent, and equitable deployment of AI technologies. Addressing these regulatory and ethical challenges is essential to safeguard participant rights, uphold scientific integrity, and fully realize the potential of AI in advancing high-quality, patient-centered clinical research.