Artificial Intelligence (AI) is rapidly reshaping the landscape of clinical research, offering transformative solutions to longstanding challenges in trial design, execution and data management. As of 2025, AI is no longer a futuristic concept; it is a practical tool driving efficiency, precision and innovation across the clinical trial ecosystem. But what does AI mean in the context of practical applications for day-to-day clinical development activities? And does its potential have any limits?

Patient recruitment remains one of the most persistent challenges in clinical research, with up to 80% of trials failing to meet enrolment timelines and nearly one-third of Phase III trials being terminated due to insufficient accrual. In this context, AI offers a compelling opportunity to reimagine how patients are identified, engaged and retained. Yet, as with any powerful tool, its use must be tempered with ethical foresight and operational realism. This editorial explores the promise, limitations and future direction of AI in clinical trial recruitment.

The Evolution of Recruitment Challenges Historically, patient recruitment has been a consistent operational challenge in clinical trials. Traditional methods including physician referrals, site databases and advertising campaigns, often resulted in slow enrolment, high dropout rates and underrepresentation of diverse populations. Despite incremental improvements, recruitment delays continue to cost sponsors millions annually and jeopardise study timelines. These persistent challenges underscore the need for innovative, data-driven approaches to recruitment, where AI, in particular, is emerging as a transformative force in this space.