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Matching patients to clinical trials with large language models
Open accessAbstract
Patient recruitment is challenging for clinical trials. We introduce TrialGPT, an end-to-end framework for zero-shot patient-to-trial matching with large language models. TrialGPT comprises three modules: it first performs large-scale filtering to retrieve candidate trials (TrialGPT-Retrieval); then predicts criterion-level patient eligibility (TrialGPT-Matching); and finally generates trial-level scores (TrialGPT-Ranking). We evaluate TrialGPT on three cohorts of 183 synthetic patients with over 75,000 trial annotations. TrialGPT-Retrieval can recall over 90% of relevant trials using less than 6% of the initial collection. Manual evaluations on 1015 patient-criterion pairs show that TrialGPT-Matching achieves an accuracy of 87.3% with faithful explanations, close to the expert performance. The TrialGPT-Ranking scores are highly correlated with human judgments and outperform the best-competing models by 43.8% in ranking and excluding trials. Furthermore, our user study reveals that TrialGPT can reduce the screening time by 42.6% in patient recruitment. Overall, these results have demonstrated promising opportunities for patient-to-trial matching with TrialGPT.
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“many published approaches remain retrospective, disease-limited, or optimized for text similarity and retrieval rather than auditable patient–trial execution over longitudinal records.”
From Transforming oncology clinical trial matching through neuro-symbolic, multi-agent AI and an oncology-specific knowledge graph: a prospective evaluation in 3804 patients (2026) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“A pioneering system in this realm is TrialGPT, which uses a trio of agents to match patients to clinical trials [36].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“clinicians assisted by TrialGPT were able to complete trial screening tasks 42.6% faster than those working without AI assistance (35.3 s vs. 61.5 s; p < 0.001)”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“the high correlation between TrialGPT’s ranking scores and human judgment suggests that MAS has the potential to serve as a reliable cognitive assistant for complex, zero-shot patient-to-trial matching”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“representing a 43.8% improvement over state-of-the-art baselines like the cross-encoder BioLinkBERT (AUROC: 0.6176)”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
Experimental — accuracy not yet measured
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“TrialGPT-Retrieval, first conducts a large-scale search to retrieve candidate trials from databases based on a patient’s profile”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
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“the system achieved a precision of 90.1% and a recall of 87.9% in locating relevant supporting sentences in clinical notes”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
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“TrialGPT-Ranking, assigns scores to rank the trials by suitability”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
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“the system achieved a precision of 90.1% and a recall of 87.9% in locating relevant supporting sentences in clinical notes”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
The cited source says what the claim attributes to it.
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“In the trial exclusion task, TrialGPT attained an AUROC of 0.7979”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
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“as these evaluations were conducted on synthetic patient records”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
The cited source says what the claim attributes to it.
In the citing paper
“manual evaluations of 1015 patient-criterion pairs demonstrated that the matching agent achieved an accuracy of 87.3%”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
The cited source says what the claim attributes to it.
In the citing paper
“TrialGPT-Retrieval successfully recalled over 90% of relevant trials while filtering out most irrelevant ones, requiring less than 6% of the total collection to maintain this high recall”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
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“TrialGPT-Matching, then evaluates the patient’s eligibility against each trial’s criteria using LLM-based reading and annotation of inclusion/exclusion criteria”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
The cited source says what the claim attributes to it.
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“The system was then evaluated on three cohorts comprising 183 synthetic patients with over 75,000 trial eligibility annotations.”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [36]
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Metadata
- Authors
- Jin, Q., Wang, Z., Floudas, C. S., Chen, F., Gong, C., Bracken-Clarke, D., Xue, E., Yang, Y., Sun, J., & Lu, Z.
- Affiliations
- National Institutes of Health (US)
- University of Illinois Urbana-Champaign (US)
- University of Pittsburgh (US)
- Albert Einstein College of Medicine (US)
- Corresponding author
- Lu, Zhiyong
- ORCID
- Venue
- Nature Communications
- Published
- 2024-11-18
- Volume
- 15
- Issue
- 1
- Pages
- 9074-9074
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC11574183
- OpenAlex
- W4404486407
- Cited by
- 287
- References
- 43
- Citations by year
- 2026: 119 · 2025: 117 · 2024: 50
- FWCI
- 26.74
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- PubMed Central (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University) (cc-by)
- Funders
- U.S. Department of Health & Human Services | National Institutes of Health
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Keywords
- clinical trial recruitment, large language models, explainable AI, synthetic patient data, user study
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