The Economics of Clinical Trials in the Age of AI

Clinical trials are the cornerstone of medical innovation, yet they remain shackled by outdated processes. For sponsors managing these trials, the stakes couldn't be higher: each day of delay costs millions in development expenses and lost market opportunity. As sponsors ourselves, we are now seeing firsthand how artificial intelligence is breaking these chains, particularly for previously neglected rare diseases.

Clinical trials are heavily human-driven. Sponsors, CROs, and sites all operate within their own frameworks, but the burden of execution primarily falls on site coordinators. They are responsible for patient recruitment, study visits, data management, compliance, budgeting, billing and liaising with multiple stakeholders, all while juggling multiple trials. This disproportionate burden on sites trickles back up to both sponsors and CROs in terms of delays, errors, and increased operational costs. Historically, sponsors have had minimal visibility into site-level execution, instead relying on periodic updates.

AI is changing this by introducing real-time monitoring, automation, and intelligent decision support at every stage of a clinical trial using guided workflows. Rather than leaving coordinators to interpret complex protocol documents and keep up with amendments manually, AI teammates can break down the protocol into step-by-step flows tied to each visit, procedure, and documentation task.

For example, when a protocol update or ICF revision occurs, AI can alert the site and update the relevant workflow steps, ensuring coordinators follow the correct version and capture all required re-consents or assessments. Instead of relying on static trackers or periodic CRA check-ins, AI can keep the workflow dynamically aligned with the latest protocol version. This ensures real-time compliance and reduces protocol deviations without additional overhead.

These guided workflows also integrate logic from the protocol (such as inclusion/exclusion rules, timing of procedures, or re-screening criteria) allowing the AI to intelligently flag issues or suggest corrective steps. Sponsors gain continuous visibility into site activity and protocol adherence, enabling faster, higher-quality decision-making without waiting for manual reconciliation or monitoring reports. Rather than relying on error-prone and cumbersome manual transcription from source documents into EDC, AI “teammates” can automatically read clinical source documentation, identify key data points, and populate the corresponding field.

For instance, after a patient visit, the coordinator may upload a vitals worksheet, lab report, or clinic note into the eSource system. The AI parses these documents, extracts structured values such as blood pressure, lab results, or adverse event descriptions, and maps them to the correct visit and form in the EDC based on protocol expectations. It also checks for completeness, ensuring that all expected fields for that visit are accounted for, and flags any omissions or inconsistencies for review. This eliminates the lag between visit completion and data entry, reduces transcription errors, and ensures that sites and sponsors have near real-time access to clean, complete data, without the need for double entry or delayed reconciliation. Instead of waiting weeks or months for monitors to detect errors, AI can flag inconsistencies in real time, such as missing informed consent prior to the first study procedure, out-of-window visit dates, or mismatches between adverse event reports and concomitant medication logs. For instance, if a serious adverse event is recorded without a corresponding update in the medication record or follow-up assessment, the AI raises an alert for the site and sponsor to investigate. These kinds of cross-domain checks are difficult to perform manually in real time but are ideal for AI validation routines.

Operational efficiency is another obvious benefit of AI. Using AI teammates, sponsors can readily develop upwards of 80 percent of a study plan (visit schedules, form libraries, data collection, etc.) The AI can parse eligibility criteria, safety assessments, and dosing schedules, and then auto-generate a calendar of required visits and assessments in a matter of minutes. This draft is then reviewed by clinical operations and data management teams to finalize the remaining details and ensure alignment with regulatory expectations. The AI accelerates the process significantly, but human teams remain responsible for review and oversight before activation. The sponsor and CRO teams can then review and manually finalize the remaining 20 percent of the study plan. In the process, the AI shortens the study timeline by weeks, if not months.

Benefits across the ecosystem

The impact of AI extends to all stakeholders in the clinical trial ecosystem. While sponsors and CROs benefit from streamlined processes, research sites experience perhaps the most immediate operational transformation. We've witnessed this firsthand at a leading ophthalmology research site utilizing AI assistance to automate and guide key operational tasks. AI teammates are used to proactively manage amendments, extract and enter data from source documents into the EDC, flag protocol deviations in real time, and auto-generate study setup documents. This task-specific support streamlines site operations, improves compliance, and enhances data quality, all while keeping human oversight central to the process. A single coordinator managed not only dozens of patients in their primary study, but also was overseeing 7-8 concurrent studies. There were over 100 patients in total and a workload that would typically require 3 to 4 staff members. AI assistance allowed the individual location to become the highest-enrolling site in the trial while maintaining quality and preventing staff burnout.

This represents substantial staffing efficiencies, with simultaneous improvements in data quality. The same research site has experienced upwards of 90 percent reduction in query burden because of improved data quality after turning to AI support. For sponsors, this ripple effect translates directly to the bottom line: trials can be executed with significantly lower costs while completing months faster.

To date, our efforts to streamline and improve clinical trials with AI is focused on the "back office" work: improved efficiencies in data management, quality control, and other activities which tie down study staff with significant administrative effort. By augmenting these individuals with AI, we are freeing them up to spend more time with the patient and focus on providing the human touch to the trial. We see this as an augmentation of the study effort, not a replacement of human staff.

From these results, and others at additional research sites (1), we believe AI is making it possible for sponsors to scale operations without proportionally increasing costs and complexity. Opus Genetics, for instance, is pursuing multiple rare disease indications using a structured, sequential approach. The ability to maintain efficiency across seven different programs will hinge on AI-driven automation that reduces redundant manual work and expedites decision-making.

AI and making rare disease treatment economically viable

The field of inherited childhood blindness has about 280 different genetic causes. At Opus, we are tackling these systematically and one by one. This requires a highly efficient, structured approach to scale and target multiple diseases in a way that is similar to a manufacturing pipeline. To achieve this, efficiency and consistency are key, and every improvement in trial execution helps accelerate progress.

One of the biggest challenges in clinical trials, particularly in rare disease research, is the cost and timeline involved. Many promising drug development programs fail simply because they cannot afford the patients, time or money required to get through clinical trials. This is why AI-powered trial management is potentially transformative for developing rare disease treatments.

In rare diseases, trials often suffer from small patient populations, making recruitment and data analysis challenging. AI-driven analytics optimize study design by identifying biomarkers that predict patient response, ensuring that trials enroll the right participants. In diabetic eye disease research conducted at the Medical University of South Carolina, machine learning helped identify patients most likely to respond to treatment based on imaging data (2). This allowed for more targeted and effective trial recruitment, reducing waste and maximizing the likelihood of successful outcomes.

Another significant advantage of AI that we have seen is its ability to enhance data integrity and regulatory compliance. AI continuously audits trial data, flagging discrepancies before they become systemic issues. This is a major improvement over traditional site monitoring, which is typically performed manually and retrospectively. There is precedence for this type of productivity gain from ophthalmic studies that utilize imaging-based AI (3, 4). Real-time image quality assessments ensure that trial data meets the highest standards before a patient even leaves the imaging center. The ability to correct errors on the spot prevents costly rework and ensures that trial data remains pristine from the outset.

AI as a competitive edge

We believe that companies that embrace AI-driven trial management today will gain a significant competitive advantage, especially as AI reasoning continues to advance. Recent benchmarks show that today's advanced AI systems can now solve complex scientific problems with accuracy approaching that of specialized human experts, a capability that was virtually nonexistent just 24 months ago (5). As some sponsors and CROs progressively adopt more and more AI functionality, a chasm will emerge between these forward-thinking organizations and those who do not keep pace.

At Opus, we think about AI adoption fundamentally as a strategic imperative rather than just a technological upgrade. The ability to use AI in clinical trials to improve efficiency enables studies for diseases that would otherwise not be treated at all. The technology is already making a tangible impact in our trials by streamlining assessments and improving quality control.

The implications of AI-powered clinical trials extend far beyond incremental efficiency gains. Our assertion is that we stand at an inflection point, one that will redefine which diseases are economically viable to treat and how quickly life-changing therapies reach patients. For sponsors, the strategic question is no longer whether to incorporate AI, but how quickly they can implement comprehensive AI strategies across their pipeline.

Forward-thinking sponsors now have the ability to ask themselves: Which diseases previously considered "too rare" or "too complex" can now be addressed? How can we reimagine our development timelines in an AI-accelerated world? And perhaps most importantly, what becomes possible when we direct the human intelligence currently consumed by manual processes toward the creative challenges of discovering novel treatments?

The sponsors who answer these questions first will not merely lead the next wave of clinical research innovation but also have the opportunity to fundamentally reset the economic equation of drug development, bringing hope to patients who have long waited for medical science to reach them.

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