Expert-level vision-language foundation model for real-world radiology and comprehensive evaluation
Sun Yat-Sen Memorial Hospital（孙逸仙纪念医院）；Sun Yat-Sen University（中山大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Peking University（北京大学）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；Xunfei Healthcare Technology Co., Ltd.（讯飞医疗科技股份有限公司）
Xiaohong Liu, Guoxing Yang, Yulin Luo, Jiaji Mao, Xiang Zhang, Haibo Wang, Zhiyang He, Ming Gao, Shanghang Zhang, Jun Shen, Guangyu Wang
Radiology is a vital and complex component of modern clinical workflow and covers many tasks. Recently, vision-language (VL) foundation models in medicine have shown potential in processing multimodal information, offering a unified solution for various radiology tasks. However, existing studies either pre-trained VL models on natural data or did not fully integrate vision-language architecture and pretraining, often neglecting the unique multimodal complexity in radiology images and their textual contexts. Additionally, their practical applicability in real-world scenarios remains underexplored. Here, we present RadFound, a large and open-source vision-language foundation model tailored for radiology, that is trained on the most extensive dataset of over 8.1 million images and 250,000 image-text pairs, covering 19 major organ systems and 10 imaging modalities. To establish expert-level multimodal perception and generation capabilities, RadFound introduces an enhanced vision encoder to capture intra-image local features and inter-image contextual information, and a unified cross-modal learning design tailored to radiology. To fully assess the models' capability, we construct a benchmark, RadVLBench, including radiology interpretation tasks like medical vision-language question-answering, as well as text generation tasks ranging from captioning to report generation. We also propose a human evaluation framework. When evaluated on the real-world benchmark involving three representative modalities, 2D images (chest X-rays), multi-view images (mammograms), and 3D images (thyroid CT scans), RadFound significantly outperforms other VL foundation models on both quantitative metrics and human evaluation. In summary, the development of RadFound represents an advancement in radiology generalists, demonstrating broad applicability potential for integration into clinical workflows.
Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)
School of Biomedical Engineering, King’s College London（伦敦国王学院生物医学工程学院）；King’s College Hospital（国王学院医院）；Institute of Psychiatry, Psychology & Neuroscience, King’s College London（伦敦国王学院精神病学、心理学与神经科学研究所）；Centre for Medical Image Computing, Dementia Research, University College London（伦敦大学学院医学影像计算中心（痴呆研究））
GreenRFM: Learning a resource-efficient radiology vision-language foundation model via supervision-centric pre-training
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China (USTC)（生物医学工程学院，生命科学与医学系，中国科学技术大学）；Center for Medical Imaging, Robotics, Analytic Computing & Learning (MIRACLE)（医学影像、机器人、分析计算与学习中心）；Suzhou Institute for Advanced Research, USTC（苏州先进研究院，中国科学技术大学）；Department of Radiology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, USTC（放射科，中国科学技术大学第一附属医院，生命科学与医学系，中国科学技术大学）；T Magnetic Resonance Translational Medicine Research Center, Department of Radiology, The First Affiliated Hospital (Southwest Hospital) of Army Medical University（7T磁共振转化医学研究中心，放射科，中国医学大学第一附属医院（西南医院））
Microsoft Health Futures UK（微软健康未来英国）；Microsoft Health Futures US（微软健康未来美国）；Mayo Clinic M.D（梅奥诊所）；Radiology AI Lab, Mayo Clinic M.D（放射学AI实验室，梅奥诊所）；Microsoft Health & Life Sciences（微软健康与生命科学）；Cambridge University Hospitals M.D（剑桥大学医院）
Curia: A Multi-Modal Foundation Model for Radiology
Raidium；Hôpital Européen Georges Pompidou, AP-HP（欧洲乔治·蓬皮杜医院, AP-HP）；Université Paris-Cité（巴黎城市大学）；INRIA（法国国家信息与自动化技术研究所）；INSERM（法国国家卫生与医学研究中心）；Beaujon Hospital（贝琼医院）；Medical University of South Carolina（南卡罗来纳医科大学）；Columbia University Irving Medical Center（哥伦比亚大学伊万斯医学中心）；Centre Cardiologique du Nord（北心脑血管中心）
Leveraging Foundation Models for Content-Based Image Retrieval in Radiology
German Cancer Research Center (DKFZ) Heidelberg, Division of Medical Image Computing（德国癌症研究中心（DKFZ）海德堡分部医学影像计算部门）；Faculty of Mathematics and Computer Science, Heidelberg University（海德堡大学数学与计算机科学学院）；Medical Faculty Heidelberg, University of Heidelberg（海德堡大学医学学院）；Department of Radiology, Charité - Universitätsmedizin Berlin（柏林夏里特医学院放射科）；German Cancer Research Center (DKFZ) Heidelberg, Interactive Machine Learning Group（德国癌症研究中心（DKFZ）海德堡交互式机器学习小组）
Homogeneous Semantic Alignment and Hierarchical Expert Routing for Radiology Report Generation
Xinjiang University（新疆大学）；Xinjiang Multimodal Intelligent Processing and Information Security Engineering Technology Research Center（新疆多模态智能处理与信息安全工程技术研究中心）；Tsinghua University（清华大学）
RadThinking: A Dataset for Longitudinal Clinical Reasoning in Radiology
Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Clinic of Radiology and Nuclear Medicine, University Hospital Basel（巴塞尔大学医院放射科与核医学科）；Department of Oncology, Johns Hopkins School of Medicine（约翰霍普金斯医学院肿瘤科）
Towards Generalist Intelligence in Dentistry: Vision Foundation Models for Oral and Maxillofacial Radiology
Shanghai Jiao Tong University（上海交通大学）；Shanghai Ninth People’s Hospital, Shanghai Jiao Tong University School of Medicine, Department of Oral Craniomaxillofacial（上海第九人民医院，上海交通大学医学院，口腔颅面医学部）；Shanghai Jiao Tong University, School of Computer Science（上海交通大学，计算机科学学院）；University of Electronic Science and Technology of China, School of Mechanical and Electrical Engineering（电子科技大学，机械与电子工程学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University, College of Stomatology（上海交通大学，口腔医学院）；National Center for Stomatology（国家口腔医学中心）；National Clinical Medical Research Center for Oral Diseases（国家口腔疾病临床医学研究中心）；Shanghai Key Laboratory of Stomatology（上海口腔医学重点实验室）；Shanghai Research Institute of Stomatology（上海口腔研究所）；Chinese Academy of Medical Science, Research Unit of Oral and Maxillofacial Regenerative Medicine（中国医学科学院，口腔颌面再生医学研究单元）
Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models
Raidium；Department of Vascular and Oncological Interventional Radiology, Hôpital Européen Georges Pompidou, AP-HP, Paris, France（巴黎欧洲乔治·蓬皮杜医院血管与肿瘤介入放射科，AP-HP）；Faculté de Santé, Université Paris-Cité, Paris, France（巴黎西岱大学健康学院）
PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology
Faculty of Dentistry, The University of Hong Kong（香港大学牙医学院）；Imperial College London（帝国理工学院）；University of Science and Technology of China（中国科学技术大学）；Department of Data and Systems Engineering, The University of Hong Kong（香港大学数据与系统工程系）；Department of Electronic Engineering, The Chinese University of Hong Kong（香港中文大学电子工程系）
Towards a clinically accessible radiology foundation model: open-access and lightweight, with automated evaluation
Microsoft Research（微软研究院）；University of Washington（华盛顿大学）；Stanford University（斯坦福大学）；University of Southern California（南加州大学）；University of California, Davis（加利福尼亚大学戴维斯分校）
Efficient Multi-Granularity Knowledge Transfer for Radiology Report Generation
Global College, Shanghai Jiao Tong University（上海交通大学全球学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
A Synthetic Data-Driven Radiology Foundation Model for Pan-tumor Clinical Diagnosis
Shanghai Jiao Tong University（上海交通大学）；Shanghai AI Lab（上海人工智能实验室）；The First Hospital of China Medical University（中国医科大学附属第一医院）；Harvard Medical School（哈佛医学院）
The Era of Foundation Models in Medical Imaging is Approaching : A Scoping Review of the Clinical Value of Large-Scale Generative AI Applications in Radiology
MedPAO-VL: Interpretable and Protocol-Compliant Agent for Structured Radiology Report Generation
Indian Institute of Technology Madras（印度马德拉斯理工学院）；Indian Institute of Science（印度科学学院）；Ramaiah Medical College and Memorial hospital（拉迈亚医学院及纪念医院）；TANUH – The AI Centre of Excellence in Healthcare, Indian Institute of Science（印度科学学院TANUH医疗人工智能卓越中心）
ReportMedSAM: Guiding Segmentation Through Radiology Reports
School of Engineering, College of Engineering and Physical Sciences, University of Birmingham（工程学院，工程与物理科学学院，伯明翰大学）；Department of Cancer and Genomic Sciences, College of Medicine and Health, University of Birmingham（癌症与基因组科学系，医学与健康学院，伯明翰大学）；Department of Computer Science, Faculty of Science and Engineering, University of Manchester（计算机科学系，科学与工程学院，曼彻斯特大学）；The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine, and Health, University of Manchester（信息学、成像与数据科学部，生物、医学与健康学院，曼彻斯特大学）
MAIRA-Seg: Enhancing Radiology Report Generation with Segmentation-Aware Multimodal Large Language Models
Microsoft Health Futures（微软健康未来）；Microsoft Research India（微软研究院印度分院）；University of Cambridge（剑桥大学）；Cambridge University Hospitals NHS Foundation Trust（剑桥大学医院国民保健信托基金）
German Cancer Research Center (DKFZ)（德国癌症研究中心）；Helmholtz Imaging, German Cancer Research Center (DKFZ)（亥姆霍兹影像，德国癌症研究中心）；Helmholtz Association（亥姆霍兹联合会）；Heidelberg University（海德堡大学）
A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation
School of Electrical and Computer Engineering, The University of Sydney（电气与计算机工程学院，悉尼大学）；School of Computer Science, The University of Adelaide（计算机科学学院，阿德莱德大学）；School of Computing and Information Technology, University of Wollongong（计算与信息科技学院，沃伦冈大学）
RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection
Department of Computing, The Hong Kong Polytechnic University（香港理工大学计算机系）；Research Institute of Trustworthy Autonomous Systems and Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学可信自主系统研究院和计算机科学与工程系）；School of Computer Science, University of Nottingham Ningbo China（宁波大学计算机学院）
Act Like a Radiologist: Radiology Report Generation across Anatomical Regions
Australian Institute for Machine Learning（澳大利亚机器学习研究所）；University of Adelaide（阿德莱德大学）；University of Technology Sydney（悉尼科技大学）；South China University of Technology（华南理工大学）；Royal Adelaide Hospital（皇家阿德莱德医院）；Flinders University（弗林德斯大学）
Enhancing the vision-language foundation model with key semantic knowledge-emphasized report refinement
Paul C. Lauterbur Research Center for Biomedical Imaging（保罗·C·劳特伯生物医学成像研究中心）；Shenzhen Institute of Advanced Technology（深圳先进技术研究院）；Pengcheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）
Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset
Stanford University（斯坦福大学）；Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福大学医学与成像人工智能中心）；Department of Electrical Engineering（电气工程系）；University of California Berkeley（加州大学伯克利分校）；Department of Radiology（放射科）；University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Hospital Israelita Albert Einstein（以色列特医院阿尔伯特·爱因斯坦医院）
Structure is Supervision: Multiview Masked Autoencoders for Radiology
Department of Computer Science, ETH Zurich（苏黎世联邦理工学院计算机科学系）；Department of Computer Science, UC Irvine（加州大学尔湾分校计算机科学系）；Department of Quantitative Biomedicine, University of Zurich（苏黎世大学定量生物医学系）
John Hopkins University（约翰霍普金斯大学）；University of Bologna（博洛尼亚大学）；Italian Institute of Technology（意大利技术研究院）；University of California, San Francisco（旧金山大学）；University of California, Berkeley（伯克利大学）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
Suppressing Prior-Comparison Hallucinations in Radiology Report Generation via Semantically Decoupled Latent Steering
University of New South Wales（新南威尔士大学）；Australian Artificial Intelligence Institute, University of Technology Sydney（澳大利亚人工智能研究所，技术悉尼大学）；Stanford University（斯坦福大学）；School of Computing and Information Technology of University of Wollongong Australia（沃林根澳大利亚大学计算与信息科技学院）；Sun Yat-sen University（中山大学）；University of Science and Technology of China（中国科学技术大学）
