Clinical trials form the backbone of medical advances but anyone who has ever worked in or around them will tell you the truth: They are incredibly long, incredibly costly, and, unfortunately, incredibly inefficient. It can take months to recruit patients alone, and dropout rates are a mammoth hurdle. But that’s where artificial intelligence (AI) is coming in—its task isn’t simply to maximize performance, but to redefine the entire life cycle.
AI is increasingly being employed to optimize everything from protocol design and site selection to patient matching and real-time data monitoring. It’s revolutionizing the way we view trials — not as static, glacially slow systems, but as flexible, intelligent systems that can adjust as they go, in real time.
I recently read a report by Roots Analysis which made me decide to maintain perspective. They have projected the AI in Clinical Trials market size from USD 1.82 billion in 2025 to USD 8.5 billion by 2035, at a CAGR of 16.7% during the forecast period. That kind of growth doesn’t occur in a vacuum because AI is actually solving real, longstanding problems in drug development.
Let’s start with recruitment. Conventional recruitment strategies absorb up to 30% of the duration of a study and still result in failure to recruit. S7 Illustrative of this is the US Community Clinical Oncology Programme, which through more than 10 years of trials was unable to recruit a representative cohort. AI does so by combing through EHRs, claims data, and social determinants of health in search of the perfect participants — often from underrepresented communities. Deep 6 AI and Trials, for instance. ai are already empowering companies to cut their recruitment times and costs.
Then there’s trial design. With the help of AI, models can replicate a variety of protocol scenarios and predict which designs are the most likely to deliver success. This balance minimises protocol amendments, which reduction is one of the most costly and time consuming stages of clinical trials.
Tools for monitoring AI-driven patient data, which could also be used during the trial, can track abnormalities in patient data on a real-time basis that allow the trial managers more quickly to respond to adverse events or dropout risk”. That kind of real-time oversight is more important when trials are spread out in decentralized trials.
But beyond efficiency, AI is also making trials more of the people and more ethical too. Algorithms trained to detect bias or underrepresentation can help sponsors design studies more equitably. This could pave the way for better, safer treatments for everyone — not only those historically overrepresented in research.
That said, we are still early on this journey. Regulatory frameworks have not completely caught up, and the “black box” nature of AI continues to be a problem. Trustwith regulators, sponsors, and patients, will depend on transparency, explainability, validation, and most importantly intention.
The thing that is most exciting to me is the prospect of AI transforming clinical trials into living, learning systems — always updating and optimizing themselves as new data comes in. That’s a long way from what most of us are familiar with — manual processes.
As the industry continues to trend toward increasingly patient-centric, data-rich models of care, AI won’t just be a tool—it will increasingly be at the core of how we conceive, execute and complete trials.
And if you’re in life sciences, or tech, or health care strategy, it’s a trend you absolutely have to be paying at