Finding and enrolling the right patients remains one of the biggest challenges in clinical research today. About a fifth of trials fail to recruit an adequate number of patients, and nearly all trials exceed their recruitment timelines. Enrollment is significantly slower now than it was five years ago. This is unsustainable. Fortunately, recent advances in AI unlock new opportunities to revolutionize these processes.
At this year’s SCOPE conference in Orlando, Liz Beatty, Co-founder and Chief Strategy Officer at Inato, moderated the panel "Driving Clinical Trial Innovation: A Collaborative Approach to AI-enabled Patient Pre-screening." Panelists included Kourosh Davarpanah, Co-founder & Chief Executive Officer at Inato; Nadir Ammour, Global Lead, Clinical Innovation and External Partnership at Sanofi; and Jovani Rivera, Site Director at Novum Research LLC. The panelists sat down to discuss their collaboration to transform patient identification, screening, and enrollment using AI. Let’s recap the journey.
The industry conversation about AI has largely focused on its growing role in drug discovery–but it holds immense potential to improve clinical trials as well. This potential took center stage in this panel discussion. Ammour emphasized,"Every component of clinical trials can be affected and advanced using AI."
Last year, Inato engaged with both research sites and sponsors to understand where AI could have the greatest impact. One challenge came up again and again. "Patient recruitment has been the number one issue raised to us, both by our sites and sponsors," said Davarpanah.
Recruitment remains one of the biggest bottlenecks in drug development, growing even more challenging as trials become larger and more complex. Beatty highlighted, "It takes 25% longer today compared to five years ago to enroll patients in trials," underscoring the urgent need for innovative solutions. Yet, the process for finding, screening, and enrolling patients remains stubbornly manual and inefficient. "Historically, chart review hasn’t changed much for many sites... it takes a lot of time," Rivera noted. Most sites still spend an hour or more reviewing a single patient for a single trial. It’s burdensome, inefficient, prone to mistakes, and impossible to scale without adding more staff.
Manual eligibility checks introduce delays that extend timelines for both sites and sponsors. These delays directly affect drug development timelines and, ultimately, how quickly patients can access new treatments. Inato saw an opportunity to reimagine this process using AI.
Inato recognized that developing an effective solution requires close collaboration with both sites and sponsors. Historically, there has often been misalignment between sites, sponsors, and vendors when it comes to tech implementation. A collaborative approach ensures that both sites and sponsors can share their needs and pain points up front to inform development and deployment. Ammour emphasized, "We partnered with sites from day one, with the goal of helping sites accelerate recruitment while lowering the burden for everyone...it cannot be an additional tax on top of everything that needs to be done."
One key priority that emerged from the three panelists: shortening time to value. There is an urgent need for process improvements, but many new solutions take months or years to get up and running, bogged down by cumbersome integrations, training, and onboarding. It was critically important to all parties in this collaboration that sites see impact early, and without jumping through a bunch of hoops.
By prioritizing this collaborative approach, Inato gained insights from both sides of clinical trials, enabling the development of a technology that benefits all stakeholders. By embedding both perspectives into the platform’s design, Inato fosters a system where sites feel empowered, sponsors see tangible results, and patients have greater opportunities to participate in trials.
Informed by site and sponsor insights, Inato quickly stood up new AI patient pre-screening capabilities to accelerate patient identification and enrollment. Patient pre-screening leverages a series of AI models to de-identify patient records, quickly determine which trials are relevant to each patient, and evaluate patients against inclusion and exclusion criteria to assess eligibility—accurately, at scale, and in compliance with HIPAA guidelines.
Early users reported that Inato reduced their pre-screening times by more than 50%, and up to 90%. Rivera reinforced the tool’s impact, stating, "What used to take weeks, we are now able to do in a day or so... it has definitely helped us meet our enrollment goals much faster, schedule patients for screening sooner, and reduce site burden and overhead."
Beyond efficiency gains, another major benefit of this tool is its ability to enable sites that traditionally lacked access to a broad range of trials to participate. AI is helping sponsors diversify their study outreach. As Rivera explained, "This [tool] allows a lot of sites that don’t have access to many other trials to participate because they have patient access. Sponsors can now see how many studies are actually reaching different sites."
While AI has already shown immense promise, panelists agreed that this is just the beginning. Ammour highlighted AI’s vast potential for accelerating clinical research: "We now have the ability to interrogate data that was previously inaccessible. This can fundamentally change how we design our programs. Instead of waiting six months or a year for insights, we can now analyze large datasets very quickly and get critical information into the right hands."
From safety reporting to analyzing patient registries, AI’s role in clinical trials continues to expand. Ammour added, "The way we run clinical trials today is not sustainable—the costs are too high, and the timelines are too long. AI can reverse this trend by scaling innovations and making processes more efficient."
AI-powered tools are reshaping clinical trial recruitment by making patient identification faster, reducing administrative burden, and increasing accessibility for both sites and sponsors. As we look to the future, continued collaboration between technology providers, sites, and sponsors will be key to unlocking AI’s full potential in clinical research.
At Inato, we are committed to empowering research sites with cutting-edge solutions that make clinical trials more accessible and efficient. Want to learn more about how AI can support your trial recruitment efforts? Click here.