Artificial Intelligence in Clinical Trials: The Present Scenario and Future Prospects

Abstract

The completion of clinical trials represents a critical phase of 10 to 15 years, with 1.5–2.0 billion USD spent during the drug development cycle. This stage not only consumes significant financial resources but also carries the weight of substantial preclinical development costs. The failure of a clinical trial results in a staggering loss ranging from 800 million to 1.4 billion USD, underscoring the high stakes involved in drug development. Two primary contributors to the elevated trial failure rates are suboptimal patient cohort selection and recruiting methods, along with challenges in effectively monitoring patients throughout trials. Remarkably, only one out of every ten compounds entering a clinical trial successfully makes it on the market. AI holds the promise to revolutionize key aspects of clinical trial design, ultimately leading to a substantial increase in trial success rates. By leveraging AI, improvements can be made in patient cohort selection, refining recruitment techniques, and enhancing real-time monitoring during trials. The integration of AI in these pivotal stages of clinical trials offers a pathway to mitigate the financial risks associated with trial failure, fostering a more efficient and effective drug development process. This book chapter delves into the application of AI techniques, including DL, NLP, DeepQA technology, DRL, HMI, and other advanced methodologies in the context of clinical trials. This abstract provides an overview of how AI interventions can reshape the landscape of clinical trials, offering a glimpse into the present scenario and prospects at the intersection of artificial intelligence and drug development.