ArticleNature communications2024
Matching patients to clinical trials with large language models.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 151 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
151 citing papers in PubMed.
- Leveraging electronic health records for atrial fibrillation cohort generation.Health information science and systems · 2026Article
- The daily dose: Early usability of an LLM tool for patient summaries and trial matching in radiation oncology.Clinical and translational radiation oncology · 2026Article
- How to apply artificial intelligence (AI) to facilitate and enhance MASH trials.Hepatology international · 2026Review
- LungGPT: A unified multimodal system for interpretable diagnosis and clinical decision support of respiratory diseases.Cell reports. Medicine · 2026Article
- From Invisible to Enrolled: How Artificial Intelligence Is Reshaping Clinical Trial Recruitment.Pharmaceutical medicine · 2026Article
- Large language models in oncology: promise, pitfalls, and the path to real-world adoption.ESMO real world data and digital oncology · 2026Article
- Automatic prediction of patients early discontinuation in oncology clinical trials.ESMO real world data and digital oncology · 2026Article
- AI-Enabled Real-World Evidence in Oncology: A Statistical Perspective for Regulatory Decisions.Therapeutic innovation & regulatory science · 2026Review
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
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- Artificial Intelligence Readiness in Clinical Trial Operations: A Narrative Review and Site-Level Governance Framework.Healthcare (Basel, Switzerland) · 2026Review
- AI-based augmentation of oncology clinical trials.Nature reviews. Clinical oncology · 2026Review
- TrialTriage, a Semiautonomous Prescreening Workflow for Resolving Ambiguity in Phase I Oncology Trial Eligibility: Development and Proof-of-Concept Study Using Synthetic Cases.JMIR formative research · 2026Article
- Generative AI, foundation models and large language models in radiation therapy physics: Clinical applications, challenges, and future directions.Medical physics · 2026Review
- Article
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- Assessment of Zero-Shot Large Language Model (LLM) Assisted Clinical Trial Matching Processes: A Metastatic Cancer Use Case.medRxiv : the preprint server for health sciences · 2026Article
- From matching to facilitation: Reframing AI's role in clinical trial enrollment.Patterns (New York, N.Y.) · 2026Article
- Review
- AI in variant analysis: fast track to genetic diagnoses.Human genetics · 2026Review
91 more citing papers are in PubMed but not listed here.
Corrections and comments
- Update of
Authors and funding
10 authors.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.