Perspectives on Artificial Intelligence in Clinical Research

AI to Address a Pivotal Time in Clinical Research

Clinical trials remain the gold standard for evaluating new medicines, but drug development is under unprecedented pressure from rising trial complexity, fragmenting patient populations, slow timelines and increasing expense. With the average time to bring a medicine to market in Europe 12-13 years and global R&D costs exceeding £1.5 billion on average per approved product¹, AI-enabled efficiencies are no longer optional in clinical development, but a strategic necessity.

The UK has the ingredients to uniquely benefit from AI-enabled trial innovation. Integrated longitudinal NHS datasets that are increasingly digitised, a progressive regulator and a strong life sciences and academic research sector together create conditions few others can match. Whilst it is sometimes challenging to separate hype from reality, several AI operational tools have already demonstrated tangible value in improving trial efficiency:

• AI-enabled patient recruitment has improved enrolment in oncology trials using structured and unstructured Electronic Health Records (EHRs) to efficiently identify eligible participants and improve enrolment rates².
• Predictive analytics can forecast site performance, dropout risk and protocol deviations allowing proactive intervention in site selection cycles. Machine learning can support efficient sample size calculations and adaptive protocols, reducing trial duration and cost².
• Natural Language Processing (NLP) can automate extraction of clinical endpoints from free-text notes improving data completeness and improving burden². Ambient Voice Technology (AVT) in NHS pilots can summarise consultations and shows how documentation and monitoring in trials can be reduced³.
• AI-supported imaging analysis has improved sensitivity and consistency of pathological and radiological endpoints, supporting eligibility assessment and endpoint evaluation².
• Multimodal data interpretation from complex trial documentation, imaging, genomics, wearables and real-world data can all be synthesized by AI in a way exceeding human analytical capacity².

AI-enabled efficiencies are no longer optional in clinical development, but a strategic necessity.

Future Applications of AI

The future of AI will move beyond optimisation of existing processes – it will reshape trial design itself, potentially even predicting outcomes, patient responder subgroups or those at greater risk of adverse events. Agentic AI systems will orchestrate complex workflows to reduce administrative burden, synthetic control arms will reduce reliance on placebo / control groups (particularly in rare diseases), and decentralised hybrid trials will become more feasible reaching diverse patient populations in more remote locations. Real-time adaptive dosing will personalise trial treatment and blur the line between research and real-word clinical care, enabling greater relevance for pricing, reimbursement and access decisions.

The patient voice matters. Initiatives such as STANDING are developing standards for diverse and inclusive datasets in healthcare AI, with a focus on improving the completeness and consistency of data and reducing bias in model development⁷.  The role of the pharmaceutical physician will increasingly become that of a steward of AI-enabled evidence, ensuring scientific integrity and patient safety remain paramount.

The role of the pharmaceutical physician will increasingly become that of a steward of AI-enabled evidence, ensuring scientific integrity and patient safety remain paramount.