DIGITAL HEALTHCARE
opportunities across the entire clinical development process. Currently, we see some of the most immediate benefits in patient recruitment, site selection, operational management and trial design. AI can help identify eligible participants faster, improve forecasting around recruitment for clinical trials, and optimise trial protocols for both efficacy and safety. In addition, there is significant potential in clinical statistical analysis where time can be compressed from weeks to days.
Looking further ahead, synthetic and simulated data could even change how clinical evidence is generated. One particularly promising area is the use of synthetic control arms for severe or rare diseases, where reducing reliance on placebo groups is both scientifically and ethically compelling.
We are already seeing encouraging examples emerge. In one study, researchers successfully recreated the control arm of a randomised clinical trial using data from previous studies and real-world patient registries, achieving comparable survival outcomes to the original trial. Approaches like this could be especially valuable for patient populations that are traditionally difficult to recruit into clinical studies, including elderly patients and those with rare diseases.
With AI, researchers could model long-term patient outcomes more effectively, reducing the need for some patients to remain on treatments for extended observational periods.
Together, these developments could reduce trial sizes, shorten timelines and accelerate access to new therapies. Importantly, before this happens researchers will need to establish sufficient regulatory confidence in the models and evidence that underpin these approaches.
Q. WHAT ROLE COULD AI AGENTS PLAY IN SCIENTIFIC RESEARCH, AND WHAT ARE THE BIGGEST BARRIERS TO ADOPTION IN BIOPHARMA R & D?
ยป I see AI agents as companions for scientists. Their value lies in helping researchers challenge assumptions, test hypotheses more rigorously and eliminate weaker ideas earlier in the process. According to our research, 38 % of organisations are already piloting AI agents in R & D.
Scientific research requires very specialised AI models that can explain their reasoning, justify conclusions and operate within established scientific principles.
To unlock the full potential of AI agents, specialised models must be trained on scientific data, supported by synthetic datasets where appropriate, and grounded in biological and molecular understanding. We also need stronger data foundations, governance and trust in AI outputs. For example, an AI model may identify a promising drug target, but scientists need confidence that the recommendation is based on complete, high-quality data and can be explained and validated. Those factors are just as important as the technology itself.
104 August 2026