How AI Is Transforming Clinical Trials

Artificial intelligence (AI) is rapidly transforming clinical trials by dramatically reducing timelines and costs, accelerating patient-centered drug development and creating more resilient and efficient trials.

Just how fast the field is evolving is illustrated in a recent CB Insights report, “AI in Clinical Development: Scouting Reports,” which provides overviews on more than 70 companies targeting clinical development workflows.

The report shows that 80% of analyzed startups use AI for automation to eliminate time-wasting inefficiencies that drive up costs. Patient recruitment cycles that used to span months are shrinking to days; study builds that took days now take minutes.

What This Means for Health Care

More than half of the companies CB Insights examined are applying AI to patient recruitment and protocol optimization. AI now is helping to realize the vision of a truly “adaptive” clinical trial, enabling real-time intervention and continuous protocol refinement through enhanced modeling and results visualization.

This gives physicians the potential to enhance their research programs in AI by cooperating actively with pharmaceutical companies and those involved with AI in various capacities. AI is aiding in drug discovery, including collecting and combining information to develop a comprehensive overview, gain knowledge about the causes and processes of diseases, identify and establish novel and existing biomarkers, generate data and models, and ultimately validate and optimize drug candidates.

More than 40% of the companies in the CB Insights scouting reports are innovating in decentralized trials or real-world evidence generation — making these two of the most common use cases behind core automation and patient matching. While distinct, these applications share a common thread: extending clinical research beyond traditional trial sites, the report notes.

How 4 Platforms Are Advancing Clinical Trials

What follows are profiles of some of the companies in the CB Insights report.

BEKHealth | Branford, Connecticut

The company uses AI-powered natural language processing to analyze structured and unstructured electronic health record (EHR) data for clinical and patient recruitment and feasibility analytics. The platform identifies protocol-eligible patients three times faster by processing health records, notes and charts with 93% accuracy, supporting site selection and trial enrollment optimization. BEKHealth's strong funding momentum and accelerating strategic partnerships were noted in the report.

Carebox | Sanford, North Carolina

This health care technology company connects patients, families and physicians with clinical treatment options through its platform for patient eligibility matching and navigation. Carebox uses AI and human-supervised automation for clinical trial patient recruitment by converting unstructured eligibility criteria into searchable indices, matching patient clinical and genomic data with relevant trials, and providing automated referral management. The platform offers trial feasibility analytics and navigation services to optimize enrollment conversion throughput and clinical development phases.

Datacubed Health | Bronx, New York

Using eClinical technology solutions for decentralized clinical trials, the platform offers electronic clinical outcomes assessments, electronic patient-reported outcomes and patient engagement platforms built on neuroeconomic principles. Datacubed uses AI to enhance patient engagement through personalized content creation and behavioral science-driven strategies. The platform applies machine learning for data analysis, patient recruitment optimization and trial management, improving retention rates and compliance through gratification and adaptive engagement technologies.

Dyania Health | Jersey City, New Jersey

The company’s AI-powered clinical trial recruitment software automates patient identification from EHRs, serving hospitals, health systems and pharmaceutical companies. Dyania Health's system identifies eligible trial candidates in minutes vs. hours of manual review, achieving 96% accuracy, the report states. The platform demonstrated 170x speed improvement at Cleveland Clinic, enabling faster enrollment across oncology, cardiology and neurology trials. The report’s authors note that what differentiates this platform in the market is that it targets clinical trial recruitment where 80% of trials miss enrollment timelines using rule-based AI leveraging medical expertise vs. pure machine learning.