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.