The Limits of Precision in Modern Clinical Trials

Introduction: The Technology Paradox

Clinical trials have never had access to more advanced technology, yet enrollment has rarely been more challenging. Across therapeutic areas, and particularly in retina, sponsors and sites now operate with sophisticated imaging platforms, AI-driven analytics, predictive modeling, and digital trial tools that would have been unimaginable a decade ago. These advances have transformed how disease is identified, measured, and monitored.

And still, enrollment timelines continue to stretch. Screen failure rates remain high. Sites struggle to meet targets despite using better tools than ever before.

This contradiction raises an important question: if technology has advanced so dramatically, why hasn’t enrollment followed the same trajectory?

The answer is not that these tools have failed. On the contrary, imaging, AI, and analytics have delivered real and measurable benefits to trial quality and scientific rigor. The challenge is that technology alone cannot resolve deeper structural and operational misalignment within the clinical trial ecosystem. Precision has improved, but scalability has not.

This article is not a critique of innovation. Instead, it is a reframing of expectations. Technology has solved many important problems, but enrollment is not purely a technological one. Until the industry acknowledges the limits of what tools can accomplish on their own, enrollment challenges will persist regardless of how advanced those tools become.

What Technology Has Solved; And What It Hasn’t

What Has Improved

There is no question that technology has meaningfully advanced clinical research. Disease characterization is now far more precise, allowing investigators to distinguish between subtypes and progression stages with greater confidence. Earlier and more accurate diagnosis has become possible, particularly in retina trials where advanced imaging enables detection before functional decline is apparent.

Endpoints are better defined and measured, reducing ambiguity and variability in data. Protocols are more standardized, improving consistency across sites and regions. These improvements have raised the overall quality and credibility of clinical trials and have helped sponsors generate clearer, more actionable results.

What Hasn’t

Despite these advances, several core constraints remain unchanged, and in some cases have worsened.

The availability of eligible patients has declined. There are more trials than ever before, but the growth of the patient population has not kept pace. Narrower criteria mean more studies are competing for the same limited pool of candidates, making enrollment a zero-sum exercise.

Site capacity and staffing constraints are another major limitation. Research staff are already stretched thin managing patient visits, data entry, image and lab submissions, query resolution, and regulatory requirements. Adding the expectation that staff will also sift through hundreds of charts to identify potential candidates is unrealistic. Physicians face similar pressures. As patient volumes increase, the time available to carefully evaluate a patient’s trial eligibility continues to shrink.

Competition between overlapping trials further compounds the issue. Sites are forced to prioritize among studies, often with limited guidance on how to allocate finite resources.

Finally, patient willingness and logistical barriers remain significant. Clinical trial participation is a substantial time commitment. Monthly visits, imaging sessions, and procedures are difficult to accommodate, especially for patients who work, have caregiving responsibilities, or lack flexible transportation.

The key takeaway is simple: technology improves selection, not supply. It helps identify the right patients, but it does not create more of them.

The Precision-Stability Tradeoff

As trials become more sophisticated, inclusion and exclusion criteria have become increasingly strict. This trend is visible across nearly every therapeutic area. For many studies, the eligible patient pool is smaller than ever before, even as the number of recruiting trials continues to grow.

Tighter criteria also introduce another challenge: some eligibility requirements cannot be assessed until a patient is formally screened. Certain measurements or lab values simply are not part of routine clinical care. In retina trials, for example, systemic values like GFR are not something a retina clinic would normally assess. As a result, patients may appear suitable based on clinical records but ultimately fail screening due to criteria that cannot be evaluated upfront.

Advanced imaging and AI tools often shift failures earlier in the process rather than eliminating them altogether. This can create the perception of efficiency, failures are identified sooner, but it does not necessarily reduce the overall screening burden. In many cases, it increases it.

More data does not automatically translate to better enrollment rates. Instead, it often means more information for staff to review, analyze, and interpret before determining eligibility. The screening process becomes more complex, not less.

This dynamic creates an illusion of efficiency. Precision increases confidence in patient selection, but it does not increase throughput. In other words, it improves certainty, not volume.

Tool Adoption Without Workflow Integration

One of the most common missteps in addressing enrollment challenges is adopting new tools in isolation. Imaging platforms, AI screening algorithms, and recruitment software are frequently implemented independently, without consideration for how they fit into existing workflows.

The result is added burden on site staff rather than relief. Each new tool introduces another system to manage, another dataset to review, and another process to maintain. More technology, without integration, means more work.

This is a critical reason why adding tools alone does not solve enrollment challenges. Without redesigning workflows, technology can actually slow screening by fragmenting processes and increasing cognitive load. Site staff are forced to move between platforms, reconcile data, and manage competing priorities; all while maintaining patient care responsibilities.

Technology should reduce burden, not add to it. When it fails to do so, adoption becomes another strain on already limited site resources.

The Human Constraint: Sites Are the Limiting Factor

At the center of every clinical trial is the site. And sites are ultimately constrained by people.

Technology often operates under the assumption of infinite site capacity;  that staff can simply absorb additional screening, data review, and administrative tasks. In reality, site personnel are finite, and many are already operating at or beyond sustainable limits.

Burnout, turnover, and cognitive overload are increasingly common. When staff are overwhelmed, even the most advanced tools cannot compensate. No algorithm can replace the judgment of an experienced coordinator or physician, and no software platform can create time where none exists.

When expectations placed on sites are misaligned with their actual capacity, enrollment suffers regardless of how advanced the technology stack may be.

Why “Better Recruitment” Isn’t the Answer

A common response to enrollment challenges is to increase recruitment efforts. More outreach, more advertising, more referrals.

But expanding recruitment does not expand eligibility.

Screening more patients does not guarantee enrolling more patients. In fact, it often inflates screen failure rates and drives up costs. Staff spend more time evaluating unsuitable candidates, burnout accelerates, and resources are consumed without improving enrollment outcomes.

Recruitment pressure treats enrollment as a volume problem when it is, in reality, a qualification and capacity problem.

What Technology Can Do When Properly Aligned

When aligned correctly, technology can play a powerful supporting role. It can assist with pre-screening, reduce redundant work, and help identify likely failures earlier without adding unnecessary burden.

Technology can also improve feasibility modeling during trial design, allowing sponsors to better understand realistic enrollment potential before studies launch.

In this role, technology acts as an enabler, supporting human judgment rather than attempting to replace it. It becomes part of an integrated system rather than a standalone solution.

Looking Forward: Alignment Over Accumulation

The future of enrollment success is not defined by accumulating more tools. It is defined by better integration.

True progress requires alignment between trial design, operational reality, site capacity, and patient availability. Without that alignment, even the most advanced technology will fall short.

Technology remains essential, but it must be deployed thoughtfully, with an understanding of human and operational constraints. Only then can innovation translate into meaningful improvements in enrollment efficiency.

The path forward is not about doing more. It is about doing better together.