AI Revolutionizes Clinical Trials: From Design Certainty to Peak Performance

The harsh reality of drug and device development is that the majority of new therapies never make it to market. Less than one in ten investigational products in non-oncology successfully navigate Phase I through regulatory approval, and this figure drops to nearly one in twenty in oncology. Operational hurdles add more difficulty, as more than 80% of trials fail to meet their enrollment targets—driven in part by increasing protocol complexity.

To address these formidable challenges, artificial intelligence (AI) is redefining the operating model for clinical research, providing a path toward improved efficiency and certainty. Leveraging unparalleled clinical trial data—including over 30,000 historical studies, 11 million patients, and 70 billion data points—AI is purpose-built to accelerate trials from the initial scientific design through flawless operational execution. This two-part approach focuses first on designing smarter protocols, and second on streamlining study start-up and managing execution for peak performance.

The End of Clinical Trial Error—AI-Powered Design for Protocol Certainty

AI is transforming clinical trial design and planning, providing crucial insights into scientific rigor and operational feasibility.

Scientific Design Enhancements

AI and statistical methods are significantly reducing the sample size and need for standard control arms:

1. Covariant Adjustment with Virtual Twins (CAVT): This approach uses AI and statistical algorithms to combine baseline patient features into a single "super covariant". By adjusting for this virtual twin, researchers can account for unexplained variability to reduce the required sample size. For instance, in a phase II Alzheimer's trial typically requiring 100 patients per arm, CAVT could realistically reduce the count to about 85 patients per arm. Regulatory bodies like the FDA and EMA are becoming more open to this approach.
2. Synthetic Control Arm (SCA)/External Controls: are created using patient-level data from historical trials, applying propensity score modeling to balance the baseline composition between the synthetic control and the investigational arm. Validation studies show that when appropriately matched, the control accurately estimates outcomes, such as overall survival, seen in a randomized control arm. Regulators are more open to accepting ECAs in diseases that are severe and lack an adequate standard of care.

Operational Protocol Optimization

The core of operational transformation is turning the study protocol from a standard document (Word or PDF) into a digital artifact. This enables:

1. Benchmarking and Complexity Quantification: AI allows new protocols to be benchmarked against relevant industry cohorts to optimize eligibility criteria and endpoints. Methodologies quantify protocol complexity through measures like the Patient Burden Index score. Data shows that a high patient burden score is associated with decreased enrollment rates and increased study dropout rates.
2. Automated Financial Planning: The digital protocol drives efficiency by enabling the automated import and standardization of the schedule of activities (SOA). These procedures are assigned standardized codes (e.g., CPT library), allowing budgets to be pre-populated with median costs. This capability can save approximately 70% of the time typically spent in budget building.

Unlock Peak Performance with AI-powered Study Start-up & Execution

The second stage of transformation focuses on tackling the operational complexity and increased cost of trial execution, where fragmented systems often lead to slow feedback loops and delays.

Accelerating Study Start-up (SSU)

AI solutions expedite the selection of optimal sites and automate foundational tasks.

1. Predictive Site Selection: AI models take study characteristics (indication, phase, disease severity) as input to recommend sites best suited for the trial based on historical and current performance. Sites predicted to be high performers enroll on average five times faster than low performers. Deploying these prioritized sites can lead to an average three-month acceleration in cumulative enrollment timelines, helping trials achieve up to a 37% faster enrollment rate relative to typical industry benchmarks.
2. Database Build Automation: AI is automating the generation of the EDC study build, including forms, edit checks, and test cases. This moves toward a "one-click study build" capability, yielding a projected 75% improvement in time savings. The typical 12-to-16-week database build process is anticipated to be reduced to just three or four weeks.

Continuous Optimization in Execution

During the execution phase, AI ensures real-time oversight and automation of core operational workflows.

1. Dynamic Enrollment Forecasting: AI establishes a baseline enrollment forecast based on historical cohort performance and then dynamically updates the trajectory based on real-time performance. This provides actionable insights, letting teams course correct proactively.
2. Payment Automation: Integrating clinical and operational data (triggers from EDC and eCOA) drives automated payment calculations for sites and patients (stipends, invoicing, and reimbursements). This system reduces processing time and errors, handles global tax calculations, and meets country-specific invoice regulations.
3. Streamlined Monitoring: AI helps increase CRA efficiency through streamlined monitoring workflows. Automation, including the auto-filing of letters and reports to the eTMF, results in a 40% reduction in time spent on site monitoring administration.

Shift from Reactive to Proactive

Leverage AI to de-risk your clinical strategy by generating synthetic control arms and benchmarking protocols against industry standards, ensuring optimized feasibility and reduced patient burden. Additionally, transform operational efficiency through automated budgeting and dynamic enrollment forecasting that identify high-performing sites to significantly accelerate study startup and execution.

Watch the full webinar on demand to learn more: