In the context of cancer treatment and surgery, quality of life assessment is a crucial part of determining treatment success and viability. In order to assess it, patients completed questionnaires which employ words to capture aspects of patients well-being are the norm. As the results of these questionnaires are often used to assess patient progress and to determine future treatment options, it is important to establish that the words used are interpreted in the same way by both patients and medical professionals. In this paper, we capture and model patients perceptions and associated uncertainty about the words used to describe the level of their physical function used in the highly common (in Sarcoma Services) Toronto Extremity Salvage Score (TESS) questionnaire. The paper provides detail about the interval-valued data capture as well as the subsequent modelling of the data using fuzzy sets. Based on an initial sample of participants, we use Jaccard similarity on the resulting words models to show that there may be considerable differences in the interpretation of commonly used questionnaire terms, thus presenting a very real risk of miscommunication between patients and medical professionals as well as within the group of medical professionals.
MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
Wangxuan Institute of Computer Technology, Peking University（北京大学王选计算机研究所）；Pattern Recognition Center, WeChat AI, Tencent Inc（腾讯公司微信人工智能模式识别中心）；School of Computer Science, Wuhan University（武汉大学计算机学院）
Patient-Zero: Scaling Synthetic Patient Agents to Real-World Distributions without Real Patient Data
Dept. of Comp. Sci. & Tech., Institute for AI, Tsinghua University（计算机科学与技术系，人工智能学院，清华大学）；Institute for AI Industry Research (AIR), Tsinghua University（人工智能产业研究院，清华大学）
MIPO: Mutual Integration of Patient Journey and Medical Ontology for Healthcare Representation Learning
Australian AI Institute, Faculty of Engineering and IT, University of Technology Sydney（澳大利亚人工智能研究所，工程与信息技术学院，悉尼技术大学）；School of Information Technology and Electrical Engineering, The University of Queensland（信息科技与电气工程学院，昆士兰大学）；Department of Data Science and AI, The Hong Kong Polytechnic University（数据科学与人工智能学院，香港理工大学）；Health Economics and Research Division, Department of Health and Aged Care（健康经济学与研究部，健康与老龄化部）
VeriSim: A Configurable Framework for Stress-Testing Medical AI Under Patient Communication Noise
George Mason University（乔治梅森大学）；Department of Computer Science, George Mason University（乔治梅森大学计算机科学系）；Department of Health Administration and Policy, College of Public Health, George Mason University（乔治梅森大学公共卫生学院卫生管理与政策系）；Department of Science, George Mason University（乔治梅森大学科学系）
Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies
College of Computer Science, Zhejiang University（浙江大学计算机科学与技术学院）；School of Medicine, Zhejiang University（浙江大学医学院）；Department of Biomedical Informatics, Harvard University（哈佛大学生物医学信息学系）
GraphMed-LT: Patient-Specific Graph Memory with Latent Clinical Thought Refinement for Multi-Turn Medical Conversations
School of Computing Science, University of Glasgow, UK（格拉斯哥大学计算机科学学院）；School of Natural and Computing Science, University of Aberdeen, UK（阿伯丁大学自然与计算科学学院）
Demographic Aware Probabilistic Medical Knowledge Graph Embeddings of Electronic Medical Records
University of Bonn（波恩大学）
由电子病历构建的医学知识图谱包含了丰富的患者和医疗实体信息。KG嵌入模型在这些数据上的利用已被证明对于不同的医疗任务是有效的。然而，现有的模型没有适当地纳入患者的人口统计数据，他们中的大多数忽略了医疗KG的概率特征。在本文中，我们提出了DARLING（人口统计学感知pRobabiListic medIcal kNowledge embeddinG），一个人口统计学感知的医疗KG嵌入框架，通过将患者人口统计数据与相应的超平面相关联，明确地将人口统计数据纳入医疗实体空间。我们的框架利用医疗实体内的概率特征，通过人口统计学指导来学习它们的表示。我们评估DARLING通过链接预测的治疗和药物，从EMR数据构建的医疗KG，并说明其优越的性能相比，现有的KG嵌入模型。
Simulated patient systems powered by large language model-based AI agents offer potential for transforming medical education
The Chinese University of Hong Kong（香港中文大学）；University of Michigan（密歇根大学）；Stanford University（斯坦福大学）；Mass General Brigham（麻省总医院布莱根医疗体系）；Harvard Medical School（哈佛医学院）；Cornell University（康奈尔大学）；Peking University Third Hospital（北京大学第三医院）；Shandong University（山东大学）；Qilu Hospital of Shandong University（山东大学齐鲁医院）；Rutgers University（罗格斯大学）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学布罗德研究所）；The University of Hong Kong（香港大学）；Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院）；Chinese Academy of Medical Sciences（中国医学科学院）；University of Ottawa（渥太华大学）；Peking University Sixth Hospital（北京大学第六医院）
Investigation of Customized Medical Decision Algorithms Utilizing Graph Neural Networks
Stevens Institute of Technology（史蒂文斯理工学院）；Northeastern University（东北大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；Brandeis University（布兰迪斯大学）；Carnegie Mellon University（卡内基梅隆大学）
This Treatment Works, Right? Evaluating LLM Sensitivity to Patient Question Framing in Medical QA
Northeastern University（东北大学）；UC Berkeley（加州大学伯克利分校）；UT Austin（德克萨斯大学奥斯汀分校）；UT MD Anderson Cancer Center（德克萨斯大学MD安德森癌症中心）；Georgia Institute of Technology（佐治亚理工学院）
"It Felt Like I Was Left in the Dark": Exploring Information Needs and Design Opportunities for Family Caregivers of Older Adult Patients in Critical Care Settings
Northeastern University（东北大学）；University of Michigan（密歇根大学）；University of Colorado College of Nursing（科罗拉多大学护理学院）；University of Colorado Anschutz Medical Campus（科罗拉多大学安舒茨医疗校区）
One Patient, Many Contexts: Scaling Medical AI with Contextual Intelligence
Harvard Medical School（哈佛医学院）；Clalit Research Institute（Clalit研究所）；Boston Children’s Hospital（波士顿儿童医院）；Harvard Data Science Initiative（哈佛数据科学计划）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Clalit Health Services（Clalit医疗服务集团）；Ben Gurion University（本-古里安大学）；Ben Gurion University of the Negev（内盖夫本-古里安大学）；Brigham and Women’s Hospital（布莱根妇女医院）；Harvard University（哈佛大学）；Broad Institute of MIT and Harvard（麻省理工学院与哈佛大学布罗德研究所）
