NIH-Developed Algorithm Matches Potential Volunteers to Clinical Trials

NLM's Dr. Zhiyong Lu

NIH researchers have developed an artificial intelligence (AI) algorithm to help accelerate the process of matching potential volunteers to relevant clinical research trials listed on ClinicalTrials.gov. The AI algorithm, called TrialGPT, can successfully identify relevant clinical trials for which a person is eligible and provide a summary that clearly explains how that person meets the criteria for study enrollment.

The researchers concluded this tool could help clinicians navigate the vast and ever-changing range of clinical trials available to their patients, which may lead to improved clinical trial enrollment and faster progress in medical research. The study was published in Nature Communications.

A team of researchers from NIH’s National Library of Medicine (NLM) and National Cancer Institute (NCI) harnessed the power of large language models to develop an innovative framework for TrialGPT to streamline the clinical trial matching process. TrialGPT first processes a patient summary, which contains relevant medical and demographic information. The algorithm then identifies relevant clinical trials from ClinicalTrials.gov for which a patient is eligible and excludes trials for which they are ineligible. TrialGPT then explains how the person meets the study enrollment criteria. The final output is an annotated list of clinical trials—ranked by relevance and eligibility—that clinicians can use to discuss clinical trial opportunities with their patient.

To assess how well TrialGPT predicted if a patient met a specific requirement for a clinical trial, the researchers compared TrialGPT’s results to those of three human clinicians who assessed more than 1,000 patient-criterion pairs. They found that TrialGPT achieved nearly the same level of accuracy as the clinicians.

Additionally, the researchers conducted a pilot user study, where they asked two human clinicians to review six anonymous patient summaries and match them to six clinical trials. One clinician manually assessed patient eligibility and matched them; the other clinician used TrialGPT. The researchers found that when clinicians use TrialGPT, they spent 40% less time screening patients but maintained the same level of accuracy.

Potential participants often learn about these opportunities through their clinicians. However, finding the right clinical trial for interested participants is a time-consuming and resource-intensive process, which can slow down important medical research.

NLM Senior Investigator and study author Dr. Zhiyong Lu said, “Our study shows that TrialGPT could help clinicians connect their patients to clinical trial opportunities more efficiently and save precious time that can be better spent on harder tasks that require human expertise.”