Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching
University of Kentucky（肯塔基大学）；Markey Cancer Center（马基癌症中心）
Caroline N. Leach, Mitchell A. Klusty, Samuel E. Armstrong, Justine C. Pickarski, Kristen L. Hankins, Emily B. Collier, Maya Shah, Aaron D. Mullen, V. K. Cody Bumgardner
Screening patients for clinical trial eligibility remains a manual, time-consuming, and resource-intensive process. We present a secure, scalable proof-of-concept system for Artificial Intelligence (AI)-augmented patient-trial matching that addresses key implementation challenges: integrating heterogeneous electronic health record (EHR) data, facilitating expert review, and maintaining rigorous security standards. Leveraging open-source, reasoning-enabled large language models (LLMs), the system moves beyond binary classification to generate structured eligibility assessments with interpretable reasoning chains that support human-in-the-loop review. This decision support tool represents eligibility as a dynamic state rather than a fixed determination, identifying matches when available and offering actionable recommendations that could render a patient eligible in the future. The system aims to reduce coordinator burden, intelligently broaden the set of trials considered for each patient and guarantee comprehensive auditability of all AI-generated outputs.
Matching Patients to Clinical Trials with Large Language Models
National Center for Biotechnology Information (NCBI), National Library of Medicine (NLM), National Institutes of Health (NIH)（美国国立卫生研究院（NIH）国家医学图书馆（NLM）国家生物技术信息中心（NCBI））；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Center for Cancer Research, National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所癌症研究中心）；University of Pittsburgh（匹兹堡大学）；Jacobi Medical Center, Albert Einstein College of Medicine（阿尔伯特·爱因斯坦医学院雅各比医疗中心）；University of Maryland College Park（马里兰大学帕克分校）
Recommending Clinical Trials for Online Patient Cases using Artificial Intelligence
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）
PRISM: Patient Records Interpretation for Semantic Clinical Trial Matching using Large Language Models
Triomics Research（Triomics研究院）；Medical College of Wisconsin（威斯康星医学院）；Icahn School of Medicine at Mount Sinai（西奈山伊坎医学院）；Cancer Informatics For Cancer Centers（癌症中心癌症信息学机构）；University of Pittsburgh（匹兹堡大学）
Real-world validation of a multimodal LLM-powered pipeline for High-Accuracy Clinical Trial Patient Matching leveraging EHR data
Inato（伊纳托（公司名））；Center for Research in Epidemiology and Statistics (CRESS)（流行病学与统计学研究中心）；Université Paris Cité（巴黎城市大学）；Université Sorbonne Paris Nord（巴黎北索邦大学）；INSERM（法国国家健康与医学研究院）；INRAE（法国国家农业、食品与环境研究院）；AP-HP（巴黎公共医疗集团）；Hôpital Hôtel Dieu（主宫医院）
End-To-End Clinical Trial Matching with Large Language Models
National Center for Tumor Diseases (NCT), Heidelberg University Hospital（海德堡大学医院国家肿瘤疾病中心（NCT））；Else Kroener Fresenius Center for Digital Health, Technical University Dresden（德累斯顿工业大学Else Kroener Fresenius数字健康中心）；Heidelberg University（海德堡大学）；University Hospital Dresden（德累斯顿大学医院）；University Hospital RWTH Aachen（亚琛工业大学附属医学院）；University of St Andrews（圣安德鲁斯大学）；Technical University of Munich (TUM)（慕尼黑工业大学（TUM））；Google Cloud（谷歌云）；National Center for Tumor Diseases (NCT/UCC), Dresden（德累斯顿国家肿瘤疾病中心（NCT/UCC））；German Cancer Research Center (DKFZ)（德国癌症研究中心（DKFZ））；TUD Dresden University of Technology（德累斯顿工业大学（TUD））；Helmholtz-Zentrum Dresden-Rossendorf (HZDR)（德累斯顿-罗森多夫亥姆霍兹中心（HZDR））；Gustave Roussy（古斯塔夫·鲁西癌症中心）；Paris Saclay University（巴黎-萨克雷大学）；University Hospital Aache（亚琛大学医院）
The use of large language models to enhance cancer clinical trial educational materials
Massachusetts Institute of Technology（麻省理工学院）；Technical University of Munich（慕尼黑工业大学）；Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Centaur Labs（半人马实验室）；Digidence；Loyola University Chicago（芝加哥洛约拉大学）
Lightweight Retrieval-Augmented Generation and Large Language Model-Based Modeling for Scalable Patient-Trial Matching
Department of Artificial Intelligence and Informatics, Mayo Clinic（人工智能与信息学系，梅奥诊所）；Computer Science Department, University of Tulsa（图兰大学计算机科学系）；Mayo Clinic Comprehensive Cancer Center, Mayo Clinic（梅奥诊所综合癌症中心，梅奥诊所）；Division of Community Pediatric and Adolescent Medicine, Department of Pediatrics, Mayo Clinic（社区儿科与青少年医学分会，儿科部，梅奥诊所）；Department of Neurology, Mayo Clinic（神经病学部，梅奥诊所）；Department of Radiation Oncology, Mayo Clinic（放射肿瘤学部，梅奥诊所）；Department of Quantitative Health Sciences, Mayo Clinic（定量健康科学部，梅奥诊所）
DeepEnroll: Patient-Trial Matching with Deep Embedding and Entailment Prediction
Tsinghua University（清华大学）；IQVIA；Temple University（天普大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
TrialMatchAI: An End-to-End AI-powered Clinical Trial Recommendation System to Streamline Patient-to-Trial Matching
University of Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；Oslo University Hospital（奥斯陆大学医院）；University of Oslo（奥斯陆大学）；The Netherlands Cancer Institute（荷兰癌症研究所）；University of Vic - Central University of Catalonia（维克大学-加泰罗尼亚中央大学）；European Clinical Research Infrastructure Network (ECRIN)（欧洲临床研究基础设施网络）
Towards AI-Assisted Clinical Trial Matching: Practical Considerations, Multicenter Evaluation, and Real-World Deployment
National Library of Medicine, National Institutes of Health（美国国立卫生研究院国立医学图书馆）；University of Illinois Chicago（伊利诺伊大学芝加哥分校）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；National Cancer Institute, National Institutes of Health（美国国立卫生研究院国家癌症研究所）
University of Southern Queensland（南昆士兰大学）；Royal Brisbane and Women’s Hospital（皇家布里斯班妇女医院）；Queensland University of Technology（昆士兰科技大学）；Lingnan University（岭南大学）
