AI-Assisted Protocol Design
The protocol is where most enrollment problems are created — and where they're cheapest to fix. A poorly designed protocol generates enrollment problems that compound for years; a well-designed protocol creates structural advantages that persist through the enrollment period.
The protocol design analysis
Before locking any protocol, three analyses should be performed that AI can conduct in hours rather than weeks:
Eligibility criteria analysis. For each exclusion criterion, the key question is: what fraction of the target patient population does this exclude, and is that exclusion necessary for safety or data integrity? AI analysis of real-world data sources (claims databases, EHR data if available, epidemiological databases) can estimate the population impact of each criterion. Criteria that exclude more than 20% of the eligible population without a clear scientific rationale are candidates for relaxation or removal.
Endpoint precedent analysis. For any given indication, what endpoints has FDA accepted as primary endpoints in prior approvals? What are the effect sizes and power assumptions that have supported successful NDAs? AI analysis of ClinicalTrials.gov and published FDA review packages provides this context in a fraction of the time manual literature review would take.
Competitive protocol benchmarking. What are competitor trials in the same indication doing? What eligibility criteria are they using? Where are they recruiting? If your eligibility criteria are significantly more restrictive than a competitor's without scientific justification, you're at a structural enrollment disadvantage.
The inclusion-rate calculation
One of the most valuable outputs of AI-assisted protocol analysis is the predicted inclusion rate — the estimated fraction of patients who will pass all eligibility criteria, given a realistic patient population.
A protocol with a 3% inclusion rate is not a protocol designed for successful enrollment. It may be scientifically justified, but the operational consequences need to be built into the timeline and budget. More often, inclusion rate analysis reveals criteria that can be liberalized without compromising the science.
The operational benefit
Protocol analysis that would take a clinical operations team three weeks can be done in a day with AI tools and publicly available data. The scientific judgment about what to change remains human; the data to inform that judgment is now rapidly accessible.