Multimodal large language models (MLLMs) have recently achieved remarkable progress in radiology by integrating visual perception with natural language understanding. However, they often generate clinically unsupported descriptions, known as medical hallucinations, which pose serious risks in medical applications that demand accuracy and image-grounded outputs. Through empirical analysis, we find that prompt-induced hallucinations remain prevalent in radiology MLLMs, largely due to over-sensitivity to clinical sections. To address this, we introduce Clinical Contrastive Decoding (CCD), a training-free and retrieval-free inference framework that integrates structured clinical signals from task-specific radiology expert models. CCD introduces a dual-stage contrastive mechanism to refine token-level logits during generation, thereby enhancing clinical fidelity without modifying the base MLLM. Experiments on three datasets and multiple models demonstrate that CCD consistently improves overall performance on radiology report generation (RRG). On the MIMIC-CXR dataset, it yields up to a 17% improvement in RadGraph-F1 when applied to state-of-the-art RRG models. Our approach provides a lightweight and generalisable solution for mitigating medical hallucinations, effectively bridging expert models and MLLMs in radiology.
UR$^{2}$-MLLM: Uncertainty-aware Revisit Reasoning in Multimodal Large Language Models for Radiology Report Generation
MedVisAI Lab（MedVisAI实验室）；Lee Kong Chian School of Medicine, Nanyang Technological University（南洋理工大学李光前医学院）；Centre of AI in Medicine, Singapore（新加坡医学人工智能中心）；AI Thrust, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）AI方向）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）
Volumetric Radiology AI in the Era of Multimodal Large Language Models
Fudan University（复旦大学）；Northwestern Polytechnical University（西北工业大学）；Xi’an Jiaotong University（西安交通大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Southern Medical University（南方医科大学）；The Chinese University of Hong Kong（香港中文大学）；University of British Columbia（不列颠哥伦比亚大学）；Microsoft Research（微软研究院）；Westlake University（西湖大学）
Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment
Bosch Center for Artificial Intelligence（博世人工智能中心）；Technical University of Munich（慕尼黑工业大学）；Munich Center for Machine Learning（慕尼黑机器学习中心）；Saarland University（萨尔大学）；University of Strasbourg（斯特拉斯堡大学）；IHU Strasbourg（斯特拉斯堡大学医院研究所）；CISPA Helmholtz Center for Information Security（CISPA亥姆霍兹信息安全中心）
Zooming from Context to Cue: Hierarchical Preference Optimization for Multi-Image MLLMs
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）；Tencent Youtu Lab（腾讯优图实验室）；Nanjing University（南京大学）
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）
CCS: Clinical Consensus Selection for Radiology Report Generation
School of Computing Science, University of Glasgow（格拉斯哥大学计算机科学学院）；School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；Language Technology Lab, University of Cambridge（剑桥大学语言技术实验室）
Exploring the Design Space of 3D MLLMs for CT Report Generation
Vector Institute for Artificial Intelligence（向量人工智能研究所）；Department of Biomedical Informatics, Harvard Medical School（生物医学信息学系，哈佛医学院）；Peter Munk Cardiac Centre, University Health Network（皮特·蒙克心脏中心，大学健康网络）；Medical Biophysics, University of Toronto（医学生物物理系，多伦多大学）；Department of Computer Science, University of Toronto（计算机科学系，多伦多大学）；Department of Laboratory Medicine and Pathobiology, University of Toronto（实验室医学与病理学系，多伦多大学）；AI Hub, University Health Network（人工智能中心，大学健康网络）
Representation-guided in-context learning for medical image interpretation with multimodal large language models
Schepens Eye Research Institute of Massachusetts Eye and Ear（麻省眼耳医院谢本斯眼科研究所）；Harvard Medical School（哈佛医学院）；Massachusetts Eye and Ear（麻省眼耳医院）；National Taiwan University Hospital（台湾大学医院）；Massachusetts Institute of Technology（麻省理工学院）；Northwestern University（西北大学）；Kempner Institute for the Study of Natural and Artificial Intelligence, Harvard University（哈佛大学肯普纳自然与人工智能研究所）；Harvard University（哈佛大学）；Harvard Data Science Initiative, Harvard University（哈佛大学数据科学倡议）
Synergistic Perception-Reasoning Governance: Grounding Medical MLLMs with Verifiable Anatomical Evidence
Huazhong University of Science and Technology（华中科技大学）；Imperial Global Singapore, Imperial College London（帝国理工学院新加坡全球中心）；Nanyang Technological University（南洋理工大学）；National University of Singapore（新加坡国立大学）；Anhui University（安徽大学）
Potential of Multimodal Large Language Models for Data Mining of Medical Images and Free-text Reports
Institute of Medical Research, Northwestern Polytechnical University（西北工业大学医学研究院）；School of Computing, University of Georgia（佐治亚大学计算学院）；School of Life Science and Technology, University of Electronic Science and Technology of China（电子科技大学生命科学与技术学院）；School of Automation, Northwestern Polytechnical University（西北工业大学自动化学院）；School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）；School of Biomedical Engineering, ShanghaiTech University（上海科技大学生物医学工程学院）；Lingang Laboratory（临港实验室）
Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Taipei Veterans General Hospital（台北荣民总医院）；National Yang Ming Chiao Tung University（国立阳明交通大学）；City University of Hong Kong（香港城市大学）
