Artificial Intelligence Model Matches Patients to Clinical Trials with Near Human Accuracy

Key points:
1. TrialGPT, developed by the National Institutes of Health, matches patients to clinical trials with 87.3 percent accuracy
2. Clinicians using TrialGPT screened patients 40 percent faster than manual methods without significant loss in accuracy

Patient recruitment has long been a headache for clinical research but a new tool is turning that around. The National Institutes of Health recently unveiled TrialGPT, an artificial intelligence model designed to streamline how patients are matched to clinical trials. In a large evaluation, TrialGPT achieved an 87.3 percent accuracy rate when compared to expert human reviewers, nearly on par with manual screening which typically lands between 88.7 and 90 percent. The real headline though is that clinicians using TrialGPT completed screenings 40 percent faster. The tool generates keywords for each patient’s medical record and compares them to trial criteria listed on ClinicalTrials.gov, then provides short summaries explaining the match. This helps reduce time spent sifting through endless eligibility forms. However, the system did occasionally misinterpret clinical procedures, underscoring the need for ongoing refinement. Still, with 85 percent of clinical trials facing delays due to recruitment issues, even a partial solution is welcome. The technology is being tested in real world clinical settings through a 2024 Director’s Challenge Innovation Award. Researchers are optimistic that with continued training, the model will only get better. TrialGPT could soon be a key part of how hospitals engage patients in cutting edge research. And for patients with rare or advanced conditions, that means access to experimental treatments they might otherwise miss. Matching the right person to the right study has always been the goal and now artificial intelligence is making it happen faster and smarter than ever before.

Image: PD

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