Medical text generation aims to assist with administrative work and highlight salient information to support decision-making. To reflect the specific requirements of medical text, in this paper, we propose a set of metrics to evaluate the completeness, conciseness, and attribution of the generated text at a fine-grained level. The metrics can be computed by various types of evaluators including instruction-following (both proprietary and open-source) and supervised entailment models. We demonstrate the effectiveness of the resulting framework, DocLens, with three evaluators on three tasks: clinical note generation, radiology report summarization, and patient question summarization. A comprehensive human study shows that DocLens exhibits substantially higher agreement with the judgments of medical experts than existing metrics. The results also highlight the need to improve open-source evaluators and suggest potential directions.
EmplifAI: a Fine-grained Dataset for Japanese Empathetic Medical Dialogues in 28 Emotion Labels
Kyoto Institute of Technology, Japan（京都技术大学）；National Institute of Information and Communications Technology (NICT), Japan（信息通信技术国家研究所）；Kyoto University, Japan（京都大学）；Nara Institute of Science and Technology, Japan（奈良科学技术大学）
Learning from Medical Entity Trees: An Entity-Centric Medical Data Engineering Framework for MLLMs
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, China（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，中国）；ByteDance（字节跳动）；Northeastern University（东北大学）
Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting
City University of Hong Kong（香港城市大学）；The Chinese University of Hong Kong（香港中文大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Lehigh University（里海大学）
针对医学图像数据稀缺及现有方法难以准确呈现解剖与病理细节的问题，提出结合解剖-病理提示和视觉码本细粒度图文对齐的合成模型，并在胸部 X 射线数据上验证其语义准确性。
MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging
School of Data Science, Fudan University（复旦大学数据科学学院）；Shanghai Innovation Institute（上海创新研究院）；School of Integrated Circuits, Zhejiang University（浙江大学集成电路学院）；School of Software Engineering, Huazhong University of Science and Technology（华中科技大学软件工程学院）；School of Computer Science, Northwestern Polytechnical University（西北工业大学计算机学院）
Hallucination Mitigating for Medical Report Generation
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics（南京航空航天大学人工智能学院）；MIIT Key Laboratory of Pattern Analysis and Machine Intelligence（信息产业部模式分析与机器智能重点实验室）；The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education（教育部脑机智能技术重点实验室）
Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation
City University of Hong Kong（香港城市大学）；Shenzhen University（深圳大学）；University of Rochester（罗切斯特大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；The Chinese University of Hong Kong（香港中文大学）
Stanford University（斯坦福大学）；University of Zurich（苏黎世大学）；Cornell University（康奈尔大学）
Automatic medical text simplification can assist providers with patient-friendly communication and make medical texts more accessible, thereby improving health literacy. But curating a quality corpus for this task requires the supervision of medical experts. In this work, we present $\textbf{Med-EASi}$ ($\underline{\textbf{Med}}$ical dataset for $\underline{\textbf{E}}$laborative and $\underline{\textbf{A}}$bstractive $\underline{\textbf{Si}}$mplification), a uniquely crowdsourced and finely annotated dataset for supervised simplification of short medical texts. Its $\textit{expert-layman-AI c…
Kentucky Injury Prevention and Research Center（肯塔基伤害预防与研究中心）；University of Kentucky（肯塔基大学）；Center on Drug and Alcohol Research（药物与酒精研究中心）
Surveillance of drug overdose deaths relies on death certificates for identification of the substances that caused death. Drugs and drug classes can be identified through the International Classification of Diseases, 10th Revision (ICD-10) codes present on death certificates. However, ICD-10 codes do not always provide high levels of specificity in drug identification. To achieve more fine-grained identification of substances on a death certificate, the free-text cause of death section, completed by the medical certifier, must be analyzed. Current methods for analyzing free-text death certific…
Shandong University（山东大学）；University of Amsterdam（阿姆斯特丹大学）；Tencent（腾讯）
Medical dialogue systems (MDSs) aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, treatment and consultation. The development of MDSs is hindered because of a lack of resources. In particular. (1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i.e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues. In this paper, we present ReMeDi, a set of resource for medical dialo…
SMI: Ontology-Grounded Semantic IDs for Hierarchy-Aware Medical Concept Representation
School of Electrical Engineering and Computer Science, Ohio University（俄亥俄大学电气与计算机工程学院）；University of Southern California（南加利福尼亚大学）；School of Computer Science, McGill University（麦吉尔大学计算机学院）；Northwestern University（西北大学）；University of Washington（华盛顿大学）
EMRModel: A Large Language Model for Extracting Medical Consultation Dialogues into Structured Medical Records
Taihe County People’s Hospital（太和县人民医院）；Innovation + Research Institute（创新+研究院）；GuoChuang Cloud Technology Ltd（国创云科技有限公司）；Xunfei Healthcare Technology Ltd（讯飞医疗科技有限公司）；University of Southern California（南加州大学）；The First Affiliated Hospital of Anhui Medical University（安徽医科大学第一附属医院）
MedualTime: A Dual-Adapter Language Model for Medical Time Series-Text Multimodal Learning
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Technical University of Munich（慕尼黑技术大学）；Columbia University（哥伦比亚大学）
An Empirical Analysis for Zero-Shot Multi-Label Classification on COVID-19 CT Scans and Uncurated Reports
ARTORG Center for Biomedical Engineering Research（ARTORG生物医学工程研究中心）；University of Bern（伯尔尼大学）；Bern University Hospital（伯尔尼大学医院）；Lindenhofspital（林登霍夫医院）；Campus Stiftung Lindenhof Bern（伯尔尼林登霍夫基金会校区）
OpenMedReason: Scientific Reasoning Supervision for Medical Vision-Language Models
York University（约克大学）；Vector Institute（向量研究所）；University of British Columbia（不列颠哥伦比亚大学）；University of Toronto（多伦多大学）；Unity Health Toronto / St. Michael’s Hospital（多伦多联合健康/圣迈克尔医院）；University Health Network（大学健康网络）；Arc Institute（弧研究所）；Queen's University（女王大学）
The University of Hong Kong（香港大学）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；University of Cambridge（剑桥大学）
Learning medical visual representations directly from paired radiology reports has become an emerging topic in representation learning. However, existing medical image-text joint learning methods are limited by instance or local supervision analysis, ignoring disease-level semantic correspondences. In this paper, we present a novel Multi-Granularity Cross-modal Alignment (MGCA) framework for generalized medical visual representation learning by harnessing the naturally exhibited semantic correspondences between medical image and radiology reports at three different levels, i.e., pathological r…
Beyond the common difficulties faced in the natural image captioning, medical report generation specifically requires the model to describe a medical image with a fine-grained and semantic-coherence paragraph that should satisfy both medical commonsense and logic. Previous works generally extract the global image features and attempt to generate a paragraph that is similar to referenced reports; however, this approach has two limitations. Firstly, the regions of primary interest to radiologists are usually located in a small area of the global image, meaning that the remainder parts of the ima…
The Forgotten Shield: Safety Grafting in Parameter-Space for Medical MLLMs
National University of Defense Technology（国防科技大学）；Multimodal Artificial Intelligence Systems (MAIS), Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统（MAIS））；Multimedia Department, Xiaomi Inc（小米公司多媒体部门）；Centre for Artificial Intelligence and Robotics, Hong Kong Institute of Science and Innovation, Chinese Academy of Sciences, Hong Kong（香港科学院人工智能与机器人中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences, UCAS（中国科学院大学人工智能学院）
