AI Screening Accelerates Heart Failure Clinical Trial Enrollment, Study Shows
Artificial intelligence (AI) can rapidly screen patients for clinical trial enrollment, according to a new study published in JAMA and led by Mass General Brigham researchers. Their novel AI-assisted patient screening tool significantly improved the speed of determining eligibility and enrollment in a heart failure clinical trial compared to manual screening. These findings suggest that using AI can be cheaper than conventional methods and speed up the research process, which could mean patients get earlier access to proven, effective treatments.
“Seeing this AI capability accelerate screening and trial enrollment this substantially in the context of a real-world randomized prospective trial is exciting,” said co-senior author Samuel (Sandy) Aronson, ALM, MA, executive director of IT and AI Solutions for Mass General Brigham Personalized Medicine and senior director of IT and AI Solutions for the Accelerator for Clinical Transformation.
The study randomized 4,476 patients to be either manually screened or screened using generative AI to see if they were eligible for the Co-Operative Program for Implementation of Optimal Therapy in Heart Failure (COPILOT-HF) trial.
In the AI arm of the study, a generative AI tool called RAG-Enabled Clinical Trial Infrastructure for Inclusion Exclusion Review (RECTIFIER) assessed clinical notes and other pieces of information in patients’ electronic health records to determine if they met key eligibility criteria for the heart failure study.
In the other arm of the study, research staff manually reviewed patients’ charts to determine if they met the eligibility criteria.
The rate of enrollment in the AI-enabled arm was almost double the rate of enrollment in the manual arm. This means that AI could almost halve the time it takes to complete enrollment in a trial,” said lead author Ozan Unlu, MD, a fellow in Clinical Informatics at Mass General Brigham and a fellow in Cardiovascular Medicine at Brigham and Women's Hospital.
Ref: Unlu, O et al. “Manual versus AI-Assisted Clinical Trial Screening Using Large-Language Models (MAPS-LLM)” JAMA DOI: doi:10.1001/jama.2024.28047
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