Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment

• URL: http://arxiv.org/abs/2609.01202v1
• Date: Tue, 01 Sep 2026 13:11:02 GMT
• Title: Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment
• Abstract summary: We present TrialGPT 2.0, an AI-assisted clinical trial recommendation system designed for real-world deployment.<n>Rather than asking only whether a patient may qualify, TrialGPT 2.0 assesses which trials warrant further consideration.<n>TrialGPT 2.0 retrieved at least one clinician-recommended trial in its top 10 recommendations for approximately 91% of cases.
• Score: 17.27078524771392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clinical trials are essential for advancing cancer care and drug development, but many fail because of insufficient patient enrollment. While there is growing interest in using AI to support patient recruitment, existing systems largely perform eligibility assessment alone and have rarely been evaluated in real-world oncology workflows. Here we present TrialGPT 2.0, an AI-assisted clinical trial recommendation system designed for real-world deployment. Rather than asking only whether a patient may qualify, the system also assesses which trials warrant further consideration given the patient's current clinical needs and local workflow priorities, and provides structured, inspectable explanations for expert review. Importantly, we evaluated TrialGPT 2.0 retrospectively and prospectively across multiple oncology-focused settings, spanning government, academic cancer-center, patient-advocacy, and NIH referral workflows. In retrospective multicenter cohorts comprising 288 cases, TrialGPT 2.0 retrieved at least one clinician-recommended trial in its top 10 recommendations for approximately 91% of cases while reducing clinician screening time by 55.0%. In a six-month prospective evaluation embedded in an active precision oncology tumor board, TrialGPT 2.0 contributed additional trial opportunities missed by the routine workflow, expanding patient access to clinical trial participation by 90.9%. To support scientific reproducibility, we also introduce NIH-TrialBench, a clinician-authored dataset comprising 126 diverse synthetic patient vignettes and matching scenarios from 11 NIH Institutes and Centers. Together, these results support the value of AI to assist clinical trial matching by improving clinician efficiency and identifying frequently overlooked trial opportunities, ultimately helping to expand and accelerate accrual to cancer trials.

Related papers

• ResidencyRL: Reinforcement Learning in Simulated Clinical Environments [53.76751756107077]
  We present ResidencyRL, a reinforcement learning (RL) method for training clinical artificial intelligence (AI) agents through simulated multi-turn clinical encounters.<n>On heldout evaluations, the ResidencyRL agent improves diagnostic accuracy by 7.0% under adversarial conditions.<n>Blinded expert clinicians validated these gains, preferring the trained agent in 87.6% of side-by-side comparisons.
  arXiv  Detail & Related papers  (2026-08-07T17:04:41Z)
• A Breast Vision Pathology Foundation Model for Real-world Clinical Utility [65.57568187389113]
  We present bfBRAVE, a breast-adaptive pathology foundation model developed and evaluated using a total resource of 101,638 breast wholeslide images.<n>BRAVE supported practical roles in the clinical workflow, including safe exclusion of low-risk cases from routine AI-assisted second-review rescue of initially missed positives and prioritization of cases for further assessment.
  arXiv  Detail & Related papers  (2026-05-06T07:44:39Z)
• Scalable High-Recall Constraint-Satisfaction-Based Information Retrieval for Clinical Trials Matching [6.8676517997589]
  SatIR is a scalable clinical trial retrieval method based on constraint satisfaction.<n>It retrieves 32%-72% more relevant-and-eligible trials per patient, improves recall over the union of useful trials by 22-38 points, and serves more patients with at least one useful trial.
  arXiv  Detail & Related papers  (2026-04-10T01:13:44Z)
• Cerebra: A Multidisciplinary AI Board for Multimodal Dementia Characterization and Risk Assessment [56.62016795093786]
  Cerebra is an interactive multi-agent AI team that coordinates specialized agents for EHR, clinical notes, and medical imaging analysis.<n>It supports privacy-preserving deployment by operating on structured representations and remains robust when modalities are incomplete.<n>Cerebra significantly improved expert performance, increasing accuracy by 17.5 percentage points in prospective dementia risk estimation.
  arXiv  Detail & Related papers  (2026-03-23T05:46:45Z)
• Emulating Clinician Cognition via Self-Evolving Deep Clinical Research [104.66211772795747]
  We developed DxEvolve, a self-evolving diagnostic agent that bridges these gaps through an interactive deep clinical research workflow.<n>On the MIMIC-CDM benchmark, DxEvolve improved diagnostic accuracy by 11.2% on average over backbone models.<n>By transforming experience into a governable learning asset, DxEvolve supports an accountable pathway for the continual evolution of clinical AI.
  arXiv  Detail & Related papers  (2026-03-11T11:41:51Z)
• Toward the Autonomous AI Doctor: Quantitative Benchmarking of an Autonomous Agentic AI Versus Board-Certified Clinicians in a Real World Setting [0.0]
  Globally we face a projected shortage of 11 million healthcare practitioners by 2030.<n>No end-to-end autonomous large language model (LLM)-based AI system has been rigorously evaluated in real-world clinical practice.
  arXiv  Detail & Related papers  (2025-06-27T19:04:44Z)
• Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence [4.272774624624429]
  We utilized TrialGPT to match 50 online patient cases to clinical trials and evaluate performance against traditional keyword-based searches.<n>Our results show that TrialGPT outperforms traditional methods by 46% in identifying eligible trials, with each patient, on average, being eligible for around 7 trials.
  arXiv  Detail & Related papers  (2025-04-15T21:56:36Z)
• TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets [54.98321887435557]
  This paper presents a suite of 23 meticulously curated AI-ready datasets covering multi-modal input features and 8 crucial prediction challenges in clinical trial design.<n>We provide basic validation methods for each task to ensure the datasets' usability and reliability.<n>We anticipate that the availability of such open-access datasets will catalyze the development of advanced AI approaches for clinical trial design.
  arXiv  Detail & Related papers  (2024-06-30T09:13:10Z)
• PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models [4.438101430231511]
  We present the first, end-to-end large-scale empirical evaluation of clinical trial matching using real-world EHRs. Our study showcases the capability of LLMs to accurately match patients with appropriate clinical trials.
  arXiv  Detail & Related papers  (2024-04-23T22:33:19Z)
• Matching Patients to Clinical Trials with Large Language Models [29.265158319106604]
  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)
  arXiv  Detail & Related papers  (2023-07-27T17:56:56Z)
• Utilizing ChatGPT to Enhance Clinical Trial Enrollment [2.3551878971309947]
  We propose an automated approach that leverages ChatGPT, a large language model, to extract patient-related information from unstructured clinical notes. Our empirical evaluation, conducted on two benchmark retrieval collections, shows improved retrieval performance compared to existing approaches. These findings highlight the potential use of ChatGPT to enhance clinical trial enrollment while ensuring the quality of medical service and minimizing direct risks to patients.
  arXiv  Detail & Related papers  (2023-06-03T10:54:23Z)
• Contextual Constrained Learning for Dose-Finding Clinical Trials [102.8283665750281]
  C3T-Budget is a contextual constrained clinical trial algorithm for dose-finding under both budget and safety constraints. It recruits patients with consideration of the remaining budget, the remaining time, and the characteristics of each group.
  arXiv  Detail & Related papers  (2020-01-08T11:46:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.