AI optimizes clinical trial design, accelerates patient recruitment, and enables adaptive protocols — addressing the bottlenecks that make trials take years longer and cost billions more than necessary.
AI clinical trials & research is represented by 12 published case-study records and 0 linked vendors in this banking directory. 12 records retain cited source URLs. The largest concentration is Pharmaceutical & Life Science, with Large Language Models & Generative AI the most common technology. Outcomes are attributed to each record's source when available rather than independently verified.
Limitation: Missing linked evidence is unknown and does not prove absence of capability.
Clinical trials are the most expensive and time-consuming bottleneck in bringing new treatments to patients. The average Phase 3 trial costs $255 million and takes 3-5 years, with patient recruitment alone responsible for 30% of the timeline. 80% of clinical trials fail to meet enrollment deadlines, and 20% of trial sites fail to enroll a single patient. AI is systematically addressing these inefficiencies across trial design, site selection, patient identification, data management, and regulatory compliance.
Patient recruitment AI is the highest-impact application. Machine learning models analyze EHR data across health systems to identify patients who match trial eligibility criteria — including complex inclusion/exclusion criteria that manual screening misses. Novartis, Roche, and Pfizer all deploy AI recruitment platforms that have reduced enrollment timelines by 30-50% in documented cases. AI also optimizes site selection by analyzing historical enrollment performance, patient demographics, and competing trial activity to predict which sites will enroll fastest. For rare diseases, AI enables federated data analysis across health systems to locate patients without centralizing sensitive data.
Adaptive trial designs powered by AI represent a paradigm shift in how trials are conducted. Bayesian machine learning models analyze accumulating trial data in real time, enabling modifications to dosing, sample size, patient populations, and endpoints — all while maintaining statistical rigor. The FDA has been increasingly receptive to adaptive designs, approving novel AI-enabled trial protocols. Digital biomarkers from wearables and smartphone sensors provide continuous patient data between clinic visits, reducing the burden of in-person assessments and capturing outcomes that traditional visits miss. AI also automates the massive documentation burden of clinical trials — from case report form (CRF) completion to safety signal detection and regulatory reporting — reducing the cost and error rate of trial operations.
AI analyzes structured and unstructured EHR data — diagnoses, labs, medications, clinical notes — to match patients against trial eligibility criteria automatically. This replaces the manual chart review that sites perform to find eligible patients, which is slow and incomplete. AI can scan millions of patient records in hours, identifying candidates that manual review would miss. Platforms from companies like TrialSpark, Deep 6 AI, and Tempus have demonstrated 30-50% faster enrollment in trials across oncology, rare disease, and chronic conditions. Some systems also predict patient likelihood of completing the trial, reducing dropout rates.
University of Hawaiʻi at Mānoa
Pharmaceutical companies (unnamed, via Accenture)
Undisclosed Top-10 Global Pharma
Unnamed Global Pharmaceutical Company
Bristol Myers Squibb
Undisclosed Global Pharmaceutical Company
Merck
National Institutes of Health (NIH) / National Library of Medicine
Bristol Myers Squibb
Novo Nordisk
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