AI can produce a clinical trial feasibility estimate quickly. The harder question is whether that estimate reflects what will actually happen.

A model may identify eligible patients, rank countries, and predict site enrollment. Those outputs become useful only when teams understand the assumptions behind them and the realities the data cannot capture.

Used well, AI can make feasibility more honest by helping teams:

• Test the impact of eligibility criteria
• Compare projections with previous trial performance
• Find gaps and conflicts across data sources
• Identify assumptions that need direct validation
• Show a range of likely outcomes
• Update forecasts as new evidence becomes available

The objective is a more realistic view of what must be true for the study to succeed.

Where Does AI Add Value to Trial Feasibility?

Feasibility teams often work across claims data, electronic health records, epidemiology, past trial performance, treatment patterns, site databases, and questionnaires. AI can connect these sources and find patterns that would be difficult to analyze manually.

It can model how individual eligibility criteria reduce the potential patient population, compare proposed countries, identify sites with relevant experience, and examine how similar studies performed.

AI also makes scenario testing easier. Teams can see how enrollment projections change when they adjust a laboratory threshold, treatment-history requirement, geographic footprint, or site mix.

This moves feasibility away from one fixed forecast. Decision-makers can see which assumptions have the greatest effect and where the study is most vulnerable.

How Does Data Quality Affect AI Feasibility Models?

A model cannot correct every problem in the data it receives. Missing, outdated, or inconsistent inputs can produce forecasts that appear more reliable than the underlying evidence supports.

Different sources may define the same measure in different ways. A site database might count patients seen over several years, while claims data reflects a more recent period. Electronic health records may contain detailed clinical information but miss care delivered outside the health system. Country-level epidemiology may not reflect the population served by the proposed sites.

AI can identify some of these differences, but teams still need to understand:

• Which populations each source represents
• How recently the data was collected
• Whether definitions are consistent
• Which parts of the patient journey are missing
• Whether the source reflects current standards of care
• How much confidence the data can support

The size of a dataset does not eliminate these questions. A large dataset can still produce an inaccurate forecast if it does not represent the study population or locations being considered.

Why Is Patient Availability an Incomplete Measure?

A dataset may show that eligible patients exist without showing whether a trial can recruit them.

Patients may receive care outside the proposed site network. Their physicians may not know about the study or have a referral path to a participating center. Travel, visit frequency, procedures, caregiver needs, competing trials, and trust can all affect participation.

AI can improve estimates of patient availability and highlight geographic patterns. It cannot fully determine whether patients are aware, interested, reachable, or able to participate.

Patient research, provider input, and local site knowledge are still needed to turn an eligible population into a credible recruitment forecast. The model can show where patients may be found. Human insight is needed to understand the path from identification to enrollment.

Can AI Challenge Site Enrollment Estimates?

Site-reported enrollment estimates often influence feasibility decisions, but those projections can be difficult to compare.

Sites may use different methods to estimate patient access. Some report the number of potentially eligible patients in their records. Others rely on investigator judgment, previous experience, or expected referrals. The estimates may not account consistently for competing trials, screen failures, patient willingness, or study burden.

AI can compare site projections with other evidence, including:

• Historical enrollment in similar studies
• The site’s performance in the same indication
• The effect of comparable eligibility criteria
• Patient volumes within the surrounding care network
• Current and planned competing trials
• Typical conversion from identification to screening and enrollment

This does not mean the model should automatically overrule the site. A site may have current knowledge that is not visible in available data. The comparison helps teams ask more specific questions and understand why a projection differs from the evidence around it.

Why Can Historical Site Data Be Misleading?

Past performance provides useful evidence, but site conditions change.

A site that enrolled well in a previous study may now have fewer coordinators, several competing trials, a different investigator, or limited access to the population required by the new protocol. Another site may have strong current capabilities that are not visible in historical databases.

The studies being compared may also differ in important ways. A site’s enrollment rate in a straightforward protocol may say little about its likely performance in a study with narrower eligibility, more procedures, or a longer visit schedule.

AI can improve comparisons by accounting for indication, study phase, protocol demands, startup timelines, enrollment, retention, and data quality. It can also identify where records are old, incomplete, or contradictory.

Current site interest and capacity still require direct confirmation. Historical evidence should improve that conversation, not replace it.

How Does Protocol Complexity Change the Forecast?

Feasibility models can become less reliable when they treat similar indications as similar studies.

Two trials may target the same disease while placing very different demands on patients and sites. Eligibility criteria, washout periods, required procedures, visit frequency, biomarker testing, and technology requirements can all change the recruitable population.

AI can compare protocol features with previous operational outcomes. It may identify that a certain combination of criteria has been associated with high screen-failure rates or that studies with similar visit requirements struggled with retention.

This makes the comparison more specific. Instead of asking how quickly a site enrolled in the indication, teams can ask how it performed under comparable protocol conditions.

The model still needs enough relevant historical data to support that conclusion. New modalities, rare populations, and unusual protocol designs may have few useful comparisons. In those cases, the model should make the uncertainty visible.

Should Feasibility Forecasts Show a Range?

A single enrollment number can create a false sense of certainty. Feasibility depends on several variables that will continue to change.

A more useful forecast shows a range of possible outcomes and explains what produces the difference. For example:

• The lower estimate may assume higher screen failures and slower referrals.
• The central estimate may reflect performance from the most comparable studies.
• The upper estimate may depend on strong site capacity and successful recruitment support.

AI can run these scenarios quickly and show which assumptions have the largest effect. Teams can then focus validation efforts on the variables that matter most.

A range also supports better planning. Sponsors can see where additional sites, contingency countries, referral programs, or protocol changes may be needed rather than committing to one optimistic forecast.

Can AI Expose Overly Optimistic Assumptions?

One of AI’s most valuable roles may be showing where a feasibility plan depends on assumptions that have little support.

A forecast may rely on patient counts from one source, enrollment rates from another, and site projections collected under different definitions. AI can compare those inputs and flag mismatches.

It can also examine actual performance from completed studies. If similar protocols repeatedly produced higher screen-failure rates, slower startup, or lower enrollment than sites predicted, those patterns should inform the next plan.

The purpose is not to make every forecast more conservative. It is to show where confidence is justified and where the plan depends on conditions that still need to be proven.

What Should People Still Validate?

AI cannot reliably assess every factor that affects feasibility. Teams still need current human input on:

• Site staffing and workload
• Investigator interest
• Competing studies
• Referral relationships
• Local standards of care
• Patient willingness and burden
• Operational requirements that are hard to capture in structured data

Human review is also needed to interpret conflicting evidence. A low historical enrollment rate may reflect poor site performance, an unusually difficult protocol, or a recruitment strategy that failed to reach the available population.

The model can identify the pattern. Experienced teams determine what it means and what should change.

How Can Feasibility Stay Current?

Feasibility should not end when countries and sites are selected. Startup, screening, enrollment, and site feedback provide new evidence about whether the original assumptions were correct.

AI can monitor these signals and compare actual performance with the plan. Early indicators may include slower site activation, fewer identified patients, higher screen failures, or longer gaps between referral and consent.

Teams can then update forecasts, adjust site support, strengthen referral strategies, or add capacity before delays become more difficult to recover.

This creates a learning loop between planning and execution. It also builds better evidence for future studies by recording which assumptions held up, which did not, and why.

Better Feasibility Starts With Clearer Uncertainty

AI can make feasibility faster, but its greater value is helping teams test assumptions, compare evidence, and see uncertainty earlier.

The strongest feasibility plans will combine data-driven analysis with current insight from patients, sites, providers, and operational teams. That combination gives decision-makers a clearer view of what the evidence supports, what still needs validation, and where the study plan may need to change.

A useful forecast does more than provide a number. It shows how that number was reached, how confident the team should be, and what could cause it to move.

Continue the Conversation at SCOPE Summit Europe

AI, clinical trial feasibility, patient recruitment, and data-informed study planning will be important areas of discussion at SCOPE Summit Europe. Leaders from across clinical research will explore how sponsors can combine technology with current operational insight to build more realistic and executable studies.

Learn more and register for SCOPE Summit Europe.