INTEGRATION OF ARTIFICIAL INTELLIGENCE IN ADAPTIVE TRIAL DESIGNS: ENHANCING EFFICIENCY AND PATIENT-CENTRIC OUTCOMES

Background: Integrating artificial intelligence (AI) into adaptive trial designs represents a transformative approach in clinical research, promising enhanced efficiency and accuracy in trial outcomes. This study aims to systematically review the current landscape of AI applications in adaptive clinical trial designs. Methods: A comprehensive search was conducted across multiple databases, resulting in 6177 records initially identified. After removing duplicates and ineligible records, 1476 studies were screened. Following rigorous screening and eligibility assessment, 45 studies were included in the final review. Inclusion criteria focused on peer-reviewed articles, systematic reviews, and clinical trials discussing the role of AI in adaptive trial designs. In contrast, exclusion criteria eliminated non-relevant and low-quality studies. Results: The selected studies demonstrate that AI significantly improves adaptive trial designs through advanced data analytics, predictive modelling, and real-time decision-making. AIs integration facilitates dynamic randomization, optimised dosing strategies, and efficient patient recruitment, thereby enhancing the overall effectiveness of clinical trials. Conclusion: AI integration in adaptive trial designs offers substantial benefits regarding trial efficiency, precision, and patient outcomes. Despite existing challenges such as data quality, ethical considerations, and regulatory requirements, the findings underscore the potential for AI to revolutionise clinical trials. Future research should address these challenges to harness AIs capabilities in adaptive trial designs fully.