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 trial-matching approaches remain retrospective, disease-limited, or optimized for text similarity and retrieval rather than auditable patient-trial execution over longitudinal records.
“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]
The Introduction describes prior embedding-based similarity search as non-explainable, and the Discussion says this study did not evaluate longitudinal records or EHR integration. The paper’s evaluation uses existing datasets, but these passages do not establish that many published approaches are retrospective or disease-limited. (LLM, conf=0.83)
Experimental — accuracy not yet measuredTrialGPT uses a trio of agents to match patients to clinical trials.
“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]
The Methods section (“TrialGPT”) describes a patient–trial matching architecture with three modules: Retrieval, Matching, and Ranking. This supports the three-part structure and purpose, but the paper calls them modules, not agents; it does not establish that TrialGPT uses three agents. (LLM, conf=0.85)
Experimental — accuracy not yet measuredClinicians assisted by TrialGPT completed trial screening tasks 42.6% faster than those working without AI assistance (35.3 s vs. 61.5 s; p < 0.001).
“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]
The Results section, ‘TrialGPT can reduce the screening time for patient-trial matching’ (page not provided), reports about 42.6% less screening time with TrialGPT in a pilot study involving two medical doctors. Our copy does not state the claimed overall times of 35.3 s and 61.5 s or a p-value below 0.001 for that comparison. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe 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.
“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]
The Results section reports that TrialGPT-Ranking scores correlate highly with patient–trial eligibility and that its LLM-aggregated scores correlate significantly with manual eligibility labels. The Discussion supports using TrialGPT to assist human recruiters, but the paper does not evaluate a multi-agent system (MAS); extending the finding to MAS is the citing authors’ inference, not a finding of this source. (LLM, conf=0.84)
Experimental — accuracy not yet measuredTrialGPT's AUROC of 0.7979 represents a 43.8% improvement over cross-encoder BioLinkBERT (AUROC 0.6176).
“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]
In Results (section “TrialGPT-Ranking can effectively rank and exclude candidate clinical trials”), TrialGPT’s AUROC is 0.7979 and cross-encoder BioLinkBERT’s is 0.6176. The reported 43.8% improvement refers instead to the overall average score (0.7314 versus 0.5085); the AUROC improvement over 0.6176 is about 29.2%. (LLM, conf=0.94)
Experimental — accuracy not yet measured
TrialGPT-Retrieval conducts a large-scale search to retrieve candidate trials from databases based on a patient's profile.
“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]
TrialGPT achieved a recall of 87.9% in locating relevant supporting sentences in clinical notes.
“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]
TrialGPT-Ranking assigns scores to rank the trials by suitability.
“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]
TrialGPT achieved a precision of 90.1% in locating relevant supporting sentences in clinical notes.
“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]
In the trial exclusion task, TrialGPT attained an AUROC of 0.7979.
“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]
Evaluations of TrialGPT were conducted on synthetic patient records.
“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]
Manual evaluations of 1015 patient-criterion pairs demonstrated that the matching agent achieved an accuracy of 87.3%.
“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]
In the initial pass, 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.
“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]
TrialGPT-Matching evaluates the patient's eligibility against each trial's criteria using LLM-based reading and annotation of inclusion/exclusion criteria.
“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 system was evaluated on three cohorts comprising 183 synthetic patients with over 75,000 trial eligibility annotations.
“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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“Prior systems have demonstrated that LLMs, retrieval, and ontology alignment can help identify candidate trials, 3,4,11-13 but many published approaches remain retrospective, disease-limited, or optimized for text similarity and retrieval rather than auditable patient — trial execution over…”
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Review of Large Language Models for Patient and Caregiver Support in Cancer Care Delivery
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A context-augmented large language model for accurate precision oncology medicine recommendations
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- 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 · 2023: 1
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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