Emerging Role of Artificial Intelligence in Clinical Trials: Enhancing Accuracy, Efficiency and Safety

Clinical studies are increasingly using medical imaging. AI tools and imaging biomarker calculation are later incorporated in the clinical trial setting to decrease radiological reading times and provide objectivity to the assessment of new therapy response. Clinical trial risk assessment is increasingly using artificial intelligence (AI) to increase efficiency and safety. 142 papers published between 2013 and 2024 were examined in this scoping review, with an emphasis on operational (n = 45), efficacious (n = 46), and safety (n = 55) risk prediction. For tasks including phase transition prediction, treatment impact estimation, and adverse drug event prediction, artificial intelligence (AI) approaches such as causal machine learning, deep learning (e.g., transformers, graph neural networks), and classical machine learning are employed. These techniques make use of a variety of data sources, including patient information, scholarly articles, clinical trial protocols, and molecular structures. Applications for large language models (LLMs) have increased recently; in 2023, they were used in 7 out of 33 research. While some models achieve excellent performance (AUROC up to 96%), difficulties are still, including selection bias, limited prospective research, and data quality issues. Notwithstanding these drawbacks, AI-based risk assessment has a lot of potential to revolutionise clinical trials, especially by enhancing risk-based monitoring systems. The final half of the 1.5-2.0 billion USD, 10–15-year development cycle for a single new medicine is spent on clinical trials. As a result, a failed clinical trial costs between 800 million and 1.4 billion USD, including the preclinical development expenses in addition to the trial's investment. Only one out of ten compounds that enter a clinical trial make it to market, which is due to poor recruiting and patient cohort selection strategies as well as ineffective patient monitoring during trials. We describe how current developments in artificial intelligence (AI) can be applied to improve trial success rates by reshaping important clinical trial design phases.