HEM-Med: A Hierarchical Agentic Memory Framework for Safe Multi-Center Medication Recommendation
Key Laboratory of Computing Power Network and Information Security, Ministry of Education（教育部算力网络与信息安全重点实验室）；Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算中心（国家超级计算济南中心））；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing（山东省算力互联网与服务计算重点实验室）；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）；University of Technology Sydney（悉尼科技大学）
Min Xiao, Jin Li, Shoujin Wang, Yeguang Zhen, Yishuo Li, Wenpeng Lu
Medication recommendation (MR) is critical for clinical decision support. While existing MR methods are mostly designed for single-center learning, where only one independent hospital is involved, multi-center MR with hospitals of different scales has become increasingly demanding in practice. In multi center settings, abundant data from large medical centers can improve recommendation performance for low-resource centers. Despite a few preliminary attempts on this task, two critical gaps remain. Gap 1: Existing multi-center methods fail to disentangle transferable cross-hospital clinical experience from hospital-specific prescribing patterns, limiting clinical feasibility and physician acceptance. Gap 2: They commonly overlook multi label safety constraints of MR, including drug-drug interaction risks and local formulary feasibility, which are essential for safe real-world deployment. To address these gaps, we propose HEM-Med, a novel hierarchical agentic memory framework for multi-center MR. To address Gap 1, we design a disentangled hierarchical memory module that builds patient-level medication context attribution records and disentangles them into cross hospital experience memory and hospital-specific residual memory. To address Gap 2, we introduce a safety-aware medication set generation module, which integrates dual-level experience retrieval with safety-aware set decoding to produce clinically relevant, safer, and locally adaptive medication recommendations. Experiments on a real-world multi-center eICU dataset show that HEM-Med outperforms representative transfer learning, LLM-based, and MR baselines in terms of both recommendation accuracy and safety.
MiranDa: Mimicking the Learning Processes of Human Doctors to Achieve Causal Inference for Medication Recommendation
Tohoku University（东北大学）；Graduate School of Biomedical Engineering（生物医学工程研究生院）；Graduate School of Information Sciences（信息科学研究生院）；Graduate School of Medicine（医学研究生院）；Division of Biomedical Engineering for Health and Welfare（健康与福祉生物医学工程系）；Department of Medicine and Science in Sports and Exercise（运动医学与科学系）
Knowledge-augmented Agentic AI for Mental Health Medication Information Seeking
Department of Medicine and Therapeutics, The Chinese University of Hong Kong（香港中文大学内科及药物治疗学系）；Department of Psychiatry, The Chinese University of Hong Kong（香港中文大学精神科学系）；School of Control Science and Engineering, Shandong University（山东大学控制科学与工程学院）；College of Information Science, University of Arizona（亚利桑那大学信息科学学院）；Department of Medicine, Stanford University School of Medicine, Stanford University（斯坦福大学医学院医学系）；Perelman School of Medicine, The University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；Department of Biostatistics, Vanderbilt University（范德堡大学生物统计学系）；Li Ka Shing Institute of Health Sciences, Faculty of Medicine, The Chinese University of Hong Kong（香港中文大学医学院李嘉诚健康科学研究所）
HypeMed: Enhancing Medication Recommendations with Hypergraph-Based Patient Relationships
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学耿丽学校人工智能学院）；Microsoft Research Asia（微软亚洲研究院）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索与推荐工程研究中心）；Engineering Research Center of Next-Generation Intelligent Search and Recommendation, MOE（下一代智能搜索与推荐工程研究中心，教育部）
The Chinese University of Hong Kong（香港中文大学）；DAMO Academy, Alibaba Group（阿里巴巴达摩院）；Beijing Institute of Technology（北京理工大学）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）
MeSIN: Multilevel Selective and Interactive Network for Medication Recommendation
Dalian University of Technology（大连理工大学）；Dongbei University of Finance and Economics（东北财经大学）；China National Health Development Research Center（国家卫生健康委卫生发展研究中心）
RxSafeBench: Identifying Medication Safety Issues of Large Language Models in Simulated Consultation
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；University of Electronic Science and Technology of China（电子科技大学）；Shenzhen University of Advanced Technology（深圳大学）；School of Biomedical Engineering, UNSW Sydney（生物医学工程学院，UNSW悉尼）
Department of Computer Science and Engineering, Hanyang University（韩阳大学计算机科学与工程系）；Department of Applied Artificial Intelligence, Hanyang University（韩阳大学应用人工智能系）
ARMR: Adaptively Responsive Network for Medication Recommendation
School of Computer Science and Engineering, Southeast University, Nanjing, China（东南大学计算机科学与工程学院）；Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications (Southeast University), Ministry of Education, China（新一代人工智能技术及其交叉应用关键实验室（东南大学））；The First Affiliated Hospital with Nanjing Medical University (JiangSu Province Hospital), Nanjing, China（南京医科大学第一附属医院（江苏省人民医院））
Large Language Model Distilling Medication Recommendation Model
Xi'an Jiaotong University（西安交通大学）；City University of Hong Kong（香港城市大学）；Jarvis Research Center, Tencent YouTu Lab（腾讯优图实验室贾维斯研究中心）；Southern University of Science and Technology（南方科技大学）；Jilin University（吉林大学）
SHAPE: A Sample-adaptive Hierarchical Prediction Network for Medication Recommendation
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；Peng Cheng Laboratory（鹏城实验室）；Melax Tech, Inc.（Melax科技公司）；The University of Texas Health Science Center at Houston（德克萨斯大学休斯顿健康科学中心）；Gennlife(Beijing) Technology Co Ltd（北京泛生子基因科技有限公司）
RxEval: A Prescription-Level Benchmark for Evaluating LLM Medication Recommendation
The Hong Kong University of Science and Technology（香港科技大学）；Southern University of Science and Technology（南方科技大学）；The Chinese University of Hong Kong（香港中文大学）；The Chinese University of Hong Kong, Shenzhen（香港中文大学深圳校区）；Shenzhen University General Hospital（深圳大学人民医院）
Lightweight Large Language Model for Medication Enquiry: Med-Pal
Singapore Health Services（新加坡保健服务集团）；Singapore National Eye Centre（新加坡国家眼科中心）；Singapore Eye Research Institute（新加坡眼科研究所）；Singapore General Hospital（新加坡中央医院）；University of California San Francisco（加利福尼亚大学旧金山分校）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；MOHH Holdings (Singapore) Private Limited（新加坡卫生部控股（私人）有限公司）；SingHealth Duke-NUS Family Medicine Academic Clinical Programme（新加坡保健服务集团-杜克-新加坡国立大学家庭医学学术临床项目）
RES-MR: Risk-Aware Reasoning for Explainable and Safe Medication Recommendation
Shandong Computer Science Center (National Supercomputer Center in Jinan)（山东省计算机科学中心（国家济南超级计算中心））；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；Shandong Fundamental Research Center for Computer Science（山东省计算机科学基础研究中心）；University of Technology Sydney（悉尼科技大学）
Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)
Department of Biomedical Engineering, Cullen College of Engineering（生物医学工程系，工程学院）；University of Houston（德克萨斯大学）；Department of Health Systems & Population Health Sciences, Tilman J. Fertitta Family College of Medicine（健康系统与人群健康科学系，蒂尔曼·J·费尔蒂塔家庭医学院）；University of Toronto（多伦多大学）；Virginia Tech（弗吉尼亚理工学院）
DrugBench: Evaluating AI Control Protocols for Medication Harm Mitigation
Universidad de Buenos Aires, Facultad de Ciencias Exactas y Naturales, Departamento de Computación（布宜诺斯艾利斯大学，精确与自然科学学院，计算机系）；AI Safety Argentina (AISAR)（阿根廷人工智能安全组织 (AISAR)）；Department of Computer Science, University of Oxford（牛津大学计算机科学系）；CONICET-Universidad de Buenos Aires, Instituto de Ciencias de la Computación (ICC)（阿根廷国家科学与技术研究理事会-布宜诺斯艾利斯大学，计算机科学研究所 (ICC)）
Development and Testing of a Novel Large Language Model-Based Clinical Decision Support Systems for Medication Safety in 12 Clinical Specialties
Singapore General Hospital（新加坡中央医院）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；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）
Validity of a clinical decision rule based alert system for drug dose adjustment in patients with renal failure intended to improve pharmacists' analysis of medication orders in hospitals
Paris Descartes University (Paris 5)（巴黎第五大学（巴黎笛卡尔大学））；Assistance Publique – Hôpitaux de Paris（巴黎公立医院集团）；UPMC University (Paris 06)（皮埃尔和玛丽·居里大学（巴黎第六大学））
