Quality of life assessment represents a key process of deciding treatment success and viability. As such, patients' perceptions of their functional status and well-being are important inputs for impairment assessment. Given that patient completed questionnaires are often used to assess patient status and determine future treatment options, it is important to know the level of agreement of the words used by patients and different groups of medical professionals. In this paper, we propose a measure called the Agreement Ratio which provides a ratio of overall agreement when modelling words through Fuzzy Sets (FSs). The measure has been specifically designed for assessing this agreement in fuzzy sets which are generated from data such as patient responses. The measure relies on using the Jaccard Similarity Measure for comparing the different levels of agreement in the FSs generated.
Measuring and Aligning Abstraction in Vision-Language Models with Medical Taxonomies
1 School of Computation, Information；Technology, Technical University of Munich, Germany 2 Institute of Machine Learning in Biomedical Imaging, Helmholtz Munich, Germany 3 LTCI, Télécom Paris, Institut Polytechnique de Paris, France 4 Munich Center for Machine Learning (MCML) 5 School of Biomedical Engineering；Imaging Sciences, King's College London, UK 6 Department of Artificial Intelligence in Biomedical Imaging, FAU Erlangen-Nuremberg, Germany 7 Department of Computing, Imperial College London, UK
Dissecting Role Cognition in Medical LLMs via Neuronal Ablation
School of Information, Renmin University of China（中国人民大学信息学院）；Center of Machine Learning Research, Peking University（北京大学机器学习研究中心）；School of Artificial Intelligence, Shanghai Jiaotong University（上海交通大学人工智能学院）；Health Informatics Committee of the China Society for Scientific and Technical Information, Renmin University of China（中国科学技术信息学会健康信息技术委员会，中国人民大学）；Institute for Advanced Algorithms Research (IAAR), Shanghai, China（先进算法研究所（IAAR），上海，中国）；Memtensor Research Center, Shanghai, China（Memtensor研究中心，上海，中国）
Measuring Competency, Not Performance: Item-Aware Evaluation Across Medical Benchmarks
School of Computing and Information, University of Pittsburgh（匹兹堡大学计算与信息学院）；Department of Educational Psychology, University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校教育心理学系）；Department of Emergency Medicine, University of Pittsburgh（匹兹堡大学急诊医学系）
Beyond the Hype: A dispassionate look at vision-language models in medical scenario
Bioengineering Department and Imperial-X, Imperial College London（生物工程部门和Imperial-X，帝国理工学院伦敦分校）；GSK, Artificial Intelligence and Machine Learning（GSK，人工智能与机器学习）；Cardiovascular Research Centre, Royal Brompton Hospital（心血管研究中心，皇家布里托尼医院）；School of Biomedical Engineering and Imaging Sciences, King’s College London（生物医学工程与成像科学学院，国王学院伦敦分校）
ModaLens: Measuring Image Sensitivity in Report-Conditioned Medical VLMs
Massachusetts Institute of Technology（麻省理工学院）；American International School Vienna（维也纳美国国际学校）；Substrate Labs；University of North Florida（北佛罗里达大学）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Motork；Collingwood School（科林伍德学校）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）
Measuring Outcomes in Healthcare Economics using Artificial Intelligence: with Application to Resource Management
George Mason University（乔治梅森大学）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）；Brigham and Women’s Hospital, Harvard Medical School（哈佛医学院布莱根妇女医院）
