“The journey forward, shaping the future of healthcare for generations to come, will likely unfold at the speed of trust.”

Avik Pal, CEO of CliniOps

AI is already proving its value in clinical research, processing vast datasets from EHRs, imaging, genomics and trial data at remarkable speed. Machine learning models identify patterns that might otherwise go unnoticed, while generative AI is beginning to design new molecules and predict trial success. Tasks like site selection, patient eligibility screening and safety monitoring, once major bottlenecks, can now be automated and optimised.

In theory, this should accelerate adoption. In practice, trust breaks down in several areas. First, data: AI systems are only as reliable as the data they are trained on, and clinical data is often incomplete or biased. If certain populations or events are underrepresented, outputs will reflect that imbalance. Trust in AI therefore depends on trust in the data and confidence that resulting trials remain representative.

Transparency is another barrier. Many advanced AI models operate in ways that are difficult to interpret. For regulators, auditors, researchers and clinicians, this is a major issue; if a system cannot clearly justify its conclusions, those outputs become harder to trust.

Equally, overreliance poses risks. There is a tendency to treat AI outputs as objective, when they are shaped by assumptions, training data and design. Without oversight, misplaced trust can lead to conclusions that fail under scrutiny.

This is why human oversight remains essential. AI can narrow patient cohorts, detect signals early and generate hypotheses at scale, but it cannot replace clinical judgement. Regulators continue to emphasise this balance, with rising AI-assisted submissions accompanied by increasing expectations around transparency, auditability and validation.

AI will play a central role in clinical trials, but adoption will depend less on what it can do and more on how much it is trusted. Building that trust requires better data, clearer validation and strong oversight. The pace of innovation will ultimately be set not by technology alone, but by confidence in using it responsibly.