Although large language models (LLMs) demonstrate expert-level medical knowledge, aligning their open-ended outputs with fine-grained clinician preferences remains challenging. Existing methods often rely on coarse objectives or unreliable automated judges that are weakly grounded in professional guidelines. We propose a two-stage framework to address this gap. First, we introduce HealthRubrics, a dataset of 7,034 physician-verified preference examples in which clinicians refine LLM-drafted rubrics to meet rigorous medical standards. Second, we distill these rubrics into HealthPrinciples: 119 broadly reusable, clinically grounded principles organized by clinical dimensions, enabling scalable supervision beyond manual annotation. We use HealthPrinciples for (1) offline alignment by synthesizing rubrics for unlabeled queries and (2) an inference-time tool for guided self-revision. A 30B-A3B model trained with our framework achieves 33.4% on HealthBench-Hard, outperforming much larger models including Deepseek-R1 and o3, establishing a resource-efficient baseline for clinical alignment.
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
Stanford University（斯坦福大学）；Emory University（埃默里大学）；University of California, Berkeley（加州大学伯克利分校）；University of Washington（华盛顿大学）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）
How Much Would a Clinician Edit This Draft? Evaluating LLM Alignment for Patient Message Response Drafting
Department of Computer Science, Dartmouth College（计算机科学系，达特茅斯学院）；Department of Community and Family Medicine, Dartmouth Health（社区与家庭医学系，达特茅斯健康）；The Dartmouth Institute, Dartmouth College（达特茅斯研究所，达特茅斯学院）
MedAlign: A Clinician-Generated Dataset for Instruction Following with Electronic Medical Records
Stanford School of Medicine（斯坦福大学医学院）；Stanford School of Engineering（斯坦福大学工程学院）；Stanford University（斯坦福大学）；Stanford Health Care（斯坦福医疗保健）；Vanderbilt University School of Medicine（范德比尔特大学医学院）；Vanderbilt University Medical Center（范德比尔特大学医学中心）；Hospital Israelita Albert Einstein（以色列阿尔伯特·爱因斯坦医院）
The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare
Augmenting Clinical Decision-Making with an Interactive and Interpretable AI Copilot: A Real-World User Study with Clinicians in Nephrology and Obstetrics
Peking University（北京大学）；University of Hong Kong（香港大学）；Hong Kong University of Science and Technology（香港科学与技术大学）；Peking University Third Hospital（北京大学第三医院）；Affiliated Xuzhou Municipal Hospital of Xuzhou Medical University（徐州医科大学附属徐州市人民医院）；University of Edinburgh（爱丁堡大学）；Health Data Research UK（英国健康数据研究）
Bespoke Large Language Models for Digital Triage Assistance in Mental Health Care
University of Oxford（牛津大学）；Oxford Precision Psychiatry Lab（牛津精准精神病学实验室）；NIHR Oxford Health Biomedical Research Centre（NIHR牛津健康生物医学研究中心）；Oxford Health NHS Foundation Trust（牛津健康NHS基金会信托）；University of Liverpool（利物浦大学）；Civic Health Innovation Labs（城市健康创新实验室）；Mental health Research for Innovation Centre (M-RIC)（心理健康创新研究中心）；Mersey Care NHS Foundation Trust（默西护理NHS基金会信托）
Transforming Tuberculosis Care: Optimizing Large Language Models For Enhanced Clinician-Patient Communication
School of Engineering and Technology, University of Washington Tacoma（华盛顿大学塔科马分校工程与技术学院）；Institute for Clinical Effectiveness and Health Policy（临床效果与健康政策研究所）；Centre for Research on Epidemiology and Public Health (CIESP), CONICET（CONICET流行病学与公共卫生研究中心）；School of Nursing, University of Washington Seattle（华盛顿大学西雅图分校护理学院）；School of Nursing and Healthcare Leadership, University of Washington Tacoma（华盛顿大学塔科马分校护理与医疗保健领导学院）
Empathy by Design: Aligning Large Language Models for Healthcare Dialogue
Department of Public Health Sciences, The University of Texas at El Paso, USA（公共卫生科学系，德克萨斯理工大学埃尔帕索分校）；Department of Nursing, The University of Texas at El Paso, USA（护理系，德克萨斯理工大学埃尔帕索分校）；Department of Rehabilitation Sciences, The University of Texas at El Paso, USA（康复科学系，德克萨斯理工大学埃尔帕索分校）
Fine-tuning Large Language Model (LLM) Artificial Intelligence Chatbots in Ophthalmology and LLM-based evaluation using GPT-4
Singapore National Eye Centre（新加坡全国眼科中心）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore Health Services（新加坡保健服务）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；Singapore General Hospital（新加坡中央医院）
University of Toronto（多伦多大学）；Vector Institute for AI（人工智能向量研究所）；Johns Hopkins Medical Institutions（约翰霍普金斯医学机构）；University Health Network（医疗网络）；Algoma University（阿尔戈马大学）；University of Iowa Hospitals & Clinics（爱荷华大学医院与诊所）；University of Edinburgh（爱丁堡大学）
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
From 2017 to 2018 the number of scientific publications found via PubMed search using the keyword "Machine Learning" increased by 46% (4,317 to 6,307). The results of studies involving machine learning, artificial intelligence (AI), and big data have captured the attention of healthcare practitioners, healthcare managers, and the public at a time when Western medicine grapples with unmitigated cost increases and public demands for accountability. The complexity involved in healthcare applications of machine learning and the size of the associated data sets has afforded many researchers an unco…
Designing Interpretable ML System to Enhance Trust in Healthcare: A Systematic Review to Proposed Responsible Clinician-AI-Collaboration Framework
Grado Department of Industrial & Systems Engineering, Virginia Tech（弗吉尼亚理工大学格拉多工业与系统工程系）；Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；School of Mathematics, Physics, and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）；Center for Health Research, University of Southern Queensland（南昆士兰大学健康研究中心）
Hong Kong University of Science and Technology（香港科技大学）；Massachusetts Institute of Technology（麻省理工学院）；Adobe Research（Adobe研究院）；Children’s Hospital of Zhejiang University School of Medicine（浙江大学医学院附属儿童医院）
Machine learning (ML) is increasingly applied to Electronic Health Records (EHRs) to solve clinical prediction tasks. Although many ML models perform promisingly, issues with model transparency and interpretability limit their adoption in clinical practice. Directly using existing explainable ML techniques in clinical settings can be challenging. Through literature surveys and collaborations with six clinicians with an average of 17 years of clinical experience, we identified three key challenges, including clinicians' unfamiliarity with ML features, lack of contextual information, and the nee…
Echoes of Biases: How Stigmatizing Language Affects AI Performance
Robert H. Smith School of Business, University of Maryland（马里兰大学罗伯特·H·史密斯商学院）；Simon Business School, University of Rochester（罗切斯特大学西蒙商学院）；Center for Digital Health and Artificial Intelligence (CDHAI), Carey Business School, Johns Hopkins University（约翰斯·霍普金斯大学凯瑞商学院数字健康与人工智能中心）
Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately. While reliable decision-tree methods exist to triage patients based on their presentation, those trees implicitly require human inference and are not immediately applicable in a fully automated setting. On the other hand, learning triage policies directly from experts may correct for some of the limitations of hard-coded decision-trees. In this work, we present a Deep Reinforcement Learning approach (a variant of D…
Review of multimodal machine learning approaches in healthcare
Oxford Internet Institute, University of Oxford（牛津大学互联网研究所）；Institute for People-Centred AI, University of Surrey（萨里大学以人为本人工智能研究所）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London（伦敦大学学院惠康/英国工程与物理科学研究委员会介入与外科科学中心）
Human-in-the-Loop Multi-Agent Ventilator Decision Support with Contextual Bandit Preference Learning
Shanghai University of Engineering Science, Shanghai, China Tencent Youtu Lab, Tencent, China Department of Critical Care Medicine, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Shanghai, China Department of Emergency；Critical Disease, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China Max Planck Institute for Heart；Lung Research, Bad Nauheim, Germany Fudan University, Shanghai, China BIH at Charit\'e -- Universit\"atsmedizin Berlin, Berlin, Germany
"We'll have to see how it works": An interview study to understand collaborative practices in interdisciplinary artificial intelligence and healthcare research
William Harvey Research Institute（威廉·哈维研究所）；Queen Mary University of London（伦敦玛丽女王大学）；Wolfson Institute of Population Health（沃尔夫森人口健康研究所）
Elsevier, Health and Commercial Markets（爱思唯尔，健康与商业市场部）
Clinicians face several significant barriers to search and synthesize accurate, succinct, updated, and trustworthy medical information from several literature sources during the practice of medicine and patient care. In this talk, we will be presenting our research behind the development of a Focused Clinical Search Service, powered by a Healthcare Knowledge Graph, to interpret the query intent behind clinical search queries and retrieve relevant medical snippets from a diverse corpus of medical literature.
Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives
College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China（浙江大学计算机科学与技术学院）；College of Software Technology, Zhejiang University, Ningbo 315100, China（浙江大学软件技术学院）；School of Computing, National University of Singapore, Singapore 117417（新加坡国立大学计算机学院）；Singapore General Hospital, Singapore 169608（新加坡中央医院）；National University Hospital, Singapore 119074（新加坡国立医院）
MIT（麻省理工学院）；Harvard Medical School（哈佛医学院）；Harvard Pilgrim Healthcare Institute（哈佛朝圣者医疗保健研究所）
Reinforcement learning (RL) has the potential to significantly improve clinical decision making. However, treatment policies learned via RL from observational data are sensitive to subtle choices in study design. We highlight a simple approach, trajectory inspection, to bring clinicians into an iterative design process for model-based RL studies. We identify where the model recommends unexpectedly aggressive treatments or expects surprisingly positive outcomes from its recommendations. Then, we examine clinical trajectories simulated with the learned model and policy alongside the actual hospi…
Imperial College London（伦敦帝国理工学院）；National Institute of Informatics（国立信息学研究所）
Automatically reasoning with conflicting generic clinical guidelines is a burning issue in patient-centric medical reasoning where patient-specific conditions and goals need to be taken into account. It is even more challenging in the presence of preferences such as patient's wishes and clinician's priorities over goals. We advance a structured argumentation formalism for reasoning with conflicting clinical guidelines, patient-specific information and preferences. Our formalism integrates assumption-based reasoning and goal-driven selection among reasoning outcomes. Specifically, we assume app…
Ortho Biomed Inc.（奥索生物医学公司）；University of Toronto（多伦多大学）；Worcester Polytechnic Institute (WPI)（伍斯特理工学院（WPI））；University Health Network（大学健康网络）
The aim of survival analysis in healthcare is to estimate the probability of occurrence of an event, such as a patient's death in an intensive care unit (ICU). Recent developments in deep neural networks (DNNs) for survival analysis show the superiority of these models in comparison with other well-known models in survival analysis applications. Ensuring the reliability and explainability of deep survival models deployed in healthcare is a necessity. Since DNN models often behave like a black box, their predictions might not be easily trusted by clinicians, especially when predictions are cont…
