Commentary|Articles|October 7, 2026

Using AI to Design Better Trials Before the First Patient Enrolls: Q&A with Claire Riches, Citeline

In this Q&A, Claire Riches, VP of clinical solutions at Citeline, discusses how AI is transforming endpoint selection and protocol pressure-testing, what data inputs matter most for realistic enrollment planning, and why the tools are more accessible than most sponsors assume.

Endpoint selection, inclusion-exclusion criteria, and enrollment assumptions have long been shaped by what a sponsor's internal team has seen before—a bounded view that leaves real risk invisible until a trial is already running. Artificial intelligence (AI) is changing that calculus, and for clinical operations professionals still weighing when and how to engage with these tools, the window for waiting is closing.

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© putilov_denis - © putilov_denis - stock.adobe.com
Real-time clinical trials require standardized digital data capture at the point of care through scaled eSource-EDC-EHR integration, but progress stalls because competing standards lack regulatory enforcement and one-off system integrations cannot scale beyond single studies without industry-wide alignment and legal clarity on data ownership.