AI in Clinical Research Still Has a Long Way to Go

In the pharmaceutical and biotech industries, AI is often touted as the next big disruptor in clinical research. Proponents envision algorithms accelerating trial timelines, predicting adverse events, and streamlining data analysis. Yet in the high-stakes environment of early-phase clinical trials, where safety margins are razor-thin, the reality is proving more complicated.

“There’s no question AI has potential, but I’ve seen it oversold in clinical research. The safety of participants with novel drugs is absolutely paramount as the margin for error is razor-thin,” says Dinkar Sindhu, CEO of AXIS Clinicals.

Early-phase studies are among the most complex to run. They often involve small, carefully monitored participant groups, intricate dosing schedules, and constant safety oversight. While AI models excel at finding patterns in large, structured datasets, they are less effective when working with the highly variable, fast-changing conditions that characterize these early trials.

“Everyone’s talking about how AI is going to revolutionize clinical trials, but the real breakthroughs haven’t materialized,” Sindhu notes. “What’s made the biggest difference in my experience isn’t technology for technology’s sake, it’s been doubling down on operational safety, real-time decision-making and strong site-lab integration. AI might eventually catch up, but for now, the gains are coming from systems that are proven, not promised.”

The Gap Between Promise and Performance

While AI has shown promise in later-phase studies, particularly in managing large datasets or modeling outcomes, early-phase research presents unique hurdles. Variables can shift quickly as researchers gather initial safety and efficacy data, meaning trial protocols may be amended midstream. AI tools, many of which rely on consistent data inputs, can struggle to adapt to this level of unpredictability.

Additionally, the small sample sizes of Phase I and early Phase II trials often do not provide enough data for machine learning systems to function effectively. In these cases, experienced clinical teams still outperform technology when it comes to making nuanced, real-time decisions that protect participants and keep trials on track.

Proven Systems Still Lead the Way

Despite the slow pace of AI adoption in early-phase studies, operational improvements are happening. Many CROs and trial sponsors are focusing on better integration between clinical sites and laboratories, enhanced safety monitoring protocols, and more streamlined communication systems. These measures may not carry the futuristic appeal of AI, but they are delivering measurable gains in speed, safety, and quality.

Sindhu points to this focus on “proven, not promised” systems as the real driver of progress for now. In his view, AI will likely become a more reliable asset in early-phase work, but only after significant advances in adaptability and real-time data handling. Until then, the industry’s focus should remain on strengthening processes that already work.

The Future Role of AI

While the technology is not yet ready to take the lead, its role in clinical research is expected to grow. As data integration improves and AI models are trained on larger, more relevant datasets, the technology could begin to augment the work of human experts in early-phase trials. Areas such as predictive modeling for patient recruitment, adaptive trial designs, and automated safety signal detection are likely starting points for meaningful adoption.

For now, however, the industry’s cautious approach reflects the stakes involved. In early-phase studies, speed cannot come at the expense of safety, and unproven tools must meet rigorous standards before they can be trusted with participant well-being.

The AI revolution in clinical research may still be on the horizon, but the path forward will require more than hype, it will demand solutions that can meet the exacting demands of early-phase trials without compromising safety or reliability.

About Dinkar Sindhu

As the CEO of AXIS Clinicals in the US, Dinkar leads the company’s strategic vision and global growth initiatives. Under his leadership, Axis Clinicals has strengthened its position as a trusted provider of early phase clinical trial services, advancing high-quality, regulatory-compliant solutions for pharmaceutical and biotech partners worldwide.