OBJECTIVES: This study aims to investigate the application of Artificial Intelligence (AI) in clinical trial design, focusing on optimizing patient inclusion and exclusion criteria, accelerating trial processes, and improving overall trial outcomes.

METHODS: A comprehensive literature analysis was performed to identify relevant studies on the application of AI in clinical trial design using Web of Science and PubMed databases. Key themes and insights were synthesized. The search strategy involved the following terms: "Artificial Intelligence," "clinical trial design," "efficiency," "efficacy," and "effectiveness." The study was focused on articles published from January 1, 2021, to January 1, 2024.

RESULTS: From an initial pool of 497 publications, only 10 were found to be directly relevant after screening. Key areas where AI can enhance clinical trial (CT) design were identified:

1. Predicting Patient Outcomes: AI simulations can improve statistical outcome measures, aiding precision medicine and informing trial design.
2. Predicting Trial Success: AI predictions in early research phases can enhance trial design and reduce failure rates in later stages.
3. Reshaping CT Design: AI facilitates hypothesis generation, disease understanding, drug discovery, cohort composition, monitoring, adherence, and endpoint selection.
4. Recruitment: AI tools match patients with complex inclusion criteria, improving recruitment efficiency and expanding participant reach.
5. Patient Monitoring and Adherence: AI algorithms, combined with wearable technology, enable continuous patient monitoring and real-time treatment feedback.
6. Automation for Analysis Support: AI automates data extraction and analysis, reducing manual effort and human error.

CONCLUSIONS: This study identifies key areas where AI can enhance clinical trial design, such as patient recruitment, outcome prediction, and drug adherence. However, it also highlights the critical need to address issues like data bias and ethical considerations, underscoring the importance of robust governance frameworks and collaborative efforts among stakeholders for successful AI implementation