A Multimodal Agentic Pathology Co-pilot via Evidence Grounded Reasoning
Department of Computer Science and Engineering, Hong Kong University of Science and Technology（香港科技大学计算机科学与工程系）；Department of Pathology, Nanfang Hospital, Southern Medical University（南方医科大学南芳医院病理科）；Department of Pathology, School of Basic Medical Sciences, Southern Medical University（南方医科大学基础医学学院病理科）；Department of Anatomical and Cellular Pathology, Chinese University of Hong Kong（香港中文大学解剖与细胞病理学系）；Guangdong Provincial Key Laboratory of Molecular Tumor Pathology（广东省分子肿瘤病理学重点实验室）；Jinfeng Laboratory（锦风实验室）；Department of Chemical and Biological Engineering, Hong Kong University of Science and Technology（香港科技大学化学与生物工程系）；Division of Life Science, Hong Kong University of Science and Technology（香港科技大学生命科学系）；State Key Laboratory of Nervous System Disorders, The Hong Kong University of Science and Technology（香港科技大学神经系统疾病国家重点实验室）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, The Hong Kong University of Science and Technology（香港科技大学深圳-香港协同创新研究院）
Pathology is the cornerstone of modern medicine, where accurate decision-making relies heavily on evidence-based practices. While artificial intelligence (AI) has the potential to transform clinical workflows, the intersection of AI and evidence-based medicine remains under-explored, with primitive attempts restricted to text-only general medicine. In this work, we present PathPocket, a multimodal AI agentic co-pilot designed specifically for evidence grounded pathology. We construct the most comprehensive pathology evidence corpus to date, encompassing approximately 110,472 public and authorized documents structured across a rigorous hierarchy of evidence from clinical guideline to expert opinion. From this meticulously graded foundation, we build a large-scale multimodal pathology hypergraph containing over 4.55 million entities and 7.10 million relations. Serving as a robust knowledge engine, this hypergraph provides traceable evidence for a collaborative multi-agent reasoning framework integrating input understanding, evidence retrieval, filtering, and diagnosis generation. This enables PathPocket to seamlessly resolve a wide spectrum of clinical tasks, ranging from text-only queries to complex multimodal diagnostics involving region-of-interest (ROI) and gigapixel whole-slide images (WSIs). We rigorously evaluate the system on a multidimensional benchmark of over 200,000 real-world cases, where it significantly outperforms existing state-of-the-arts. Crucially, extensive user studies demonstrate that PathPocket substantially improves the diagnostic accuracy and confidence of pathologists. By directly grounding pathology interpretations in verifiable literature, PathPocket offers a practical and scalable solution for the future of evidence grounded computational pathology.
Democratizing and accelerating AI-driven pathology research through agentic intelligence
Hong Kong University of Science and Technology（香港科技大学）；Southern Medical University（南方医科大学）；Nanfang Hospital（南方医院）；Guangdong Province Key Laboratory of Molecular Tumor Pathology（广东省分子肿瘤病理重点实验室）；Jinfeng Laboratory（金凤实验室）
Computational Pathology in the Era of Emerging Foundation and Agentic AI -- International Expert Perspectives on Clinical Integration and Translational Readiness
Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China（复旦大学附属中山医院病理科，上海交通大学医学院，上海，中国）；Department of Radiation Oncology, Stanford University School of Medicine, Stanford, CA, USA（斯坦福大学医学院放射肿瘤科，斯坦福，加利福尼亚州，美国）；Department of Basic Education, Qingdao City University, Qingdao, Shandong, China（青岛城市大学基础教育系，青岛，山东省，中国）；Department of Human Genetics, the University of Chicago, Chicago, IL, USA（芝加哥大学人类遗传学系，芝加哥，伊利诺伊州，美国）；Department of Computer Science, University of Warwick, Coventry UK（沃里克大学计算机科学系，科文特里，英国）；Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA（卡内基梅隆大学电气与计算机工程系，匹兹堡，宾夕法尼亚州，美国）；Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA（斯坦福大学医学院神经外科，斯坦福，加利福尼亚州，美国）；Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA（哈佛医学院生物医学信息学系，波士顿，马萨诸塞州，美国）
LUCAID: Agentic Multimodal AI for Lung Cancer Precision Pathology
Institute of Pathology, Charité – Universitätsmedizin Berlin（柏林夏里特医学院病理学研究所）；Freie Universität Berlin（柏林自由大学）；Humboldt-Universität zu Berlin（柏林洪堡大学）；Berlin Institute of Health at Charité – Universitätsmedizin Berlin（柏林夏里特医学院柏林卫生研究所）；Aignostics GmbH（Aignostics有限公司）；Machine Learning Group, Technical University of Berlin（柏林工业大学机器学习组）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）；MVZ HPH Institut für Pathologie und Hämatopathologie GmbH（HPH病理及血液病理诊断中心有限公司）
PathoSage: Towards Multi-Source Evidence Adjudication in Pathology via Experience-Aware Agentic Workflow
College of Computer Science, Sichuan University（四川大学计算机科学学院）；Department of Pathology and Institute of Clinical Pathology, West China Hospital, Sichuan University（四川大学华西医院病理科/临床病理研究所）；Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系）；School of Intelligent Systems Engineering, Sun Yat-sen University（中山大学智能工程学院）
From Single Chatbots to Governed Agent Ecosystems: An Agentic AI Pattern Catalogue and Orchestration Framework for Mission-Critical Hospital Information Management Systems
PathFound: An Agentic Multimodal Model Activating Evidence-seeking Pathological Diagnosis
Qing Yuan Research Institute, Shanghai Jiao Tong University（上海交通大学庆元研究院）；Shanghai Innovation Institute（上海创新研究院）；Department of Pathology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生命科学与医学学院病理科）；Intelligent Pathology Institute, Division of Life Sciences and Medicine（生命科学与医学学院智能病理研究所）；Department of Pathology, Fudan University Shanghai Cancer Center（复旦大学上海癌症中心病理科）；Department of Oncology, Shanghai Medical College, Fudan University（复旦大学上海医学院肿瘤科）；Institute of Pathology, Fudan University（复旦大学病理研究所）；Department of Pathology, The First Affiliated Hospital with Nanjing Medical University（南京医科大学第一附属医院病理科）
AutoPDE: Reliable Agentic PDE Solving via Explicitly Represented Solver Strategies
University of Science and Technology of China（中国科学技术大学）；Zhejiang University（浙江大学）；University of the Chinese Academy of Sciences（中国科学院大学）；Tsinghua University（清华大学）；Eastern Institute of Technology, Ningbo（宁波东方理工大学）
A Versatile Pathology Co-pilot via Reasoning Enhanced Multimodal Large Language Model
Hong Kong University of Science and Technology（香港科技大学）；Southern Medical University（南方医科大学）；Nanfang Hospital and School of Basic Medical Sciences（南方医院和基础医学科学学院）；Chinese University of Hong Kong（香港中文大学）
An Agentic Operationalization of DISARM for FIMI Investigation on Social Media
Center of Research Acquisition National Institute of Cyber Security Taipei City, Taiwan；Department of Mathematics Royal Military Academy Brussels, Belgium；Research Division OpenMinds Ltd. Kyiv, Ukraine；Department of Sustainable Communication Technologies SINTEF Oslo, Norway (Corresponding Author)
Toward Auditable Neuro-Symbolic Reasoning in Pathology: SQL as an Explicit Trace of Evidence
Capital Normal University, Beijing, China（首都师范大学）；Leibniz-Institut für Analytische Wissenschaften-ISAS, Dortmund, Germany（莱比锡分析科学研究所-ISAS）；Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳校区）
Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis
Department of Radiation Oncology, TUM University Hospital, Munich, Germany（放射肿瘤科，慕尼黑技术大学医院，德国）；Chair for AI in Healthcare and Medicine, Technical University of Munich (TUM)（人工智能在医疗和健康领域的主任，慕尼黑技术大学）；Chair for AI for Image-Guided Diagnosis and Therapy, Technical University of Munich (TUM)（人工智能在影像引导诊断和治疗领域的主任，慕尼黑技术大学）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML），德国慕尼黑）；Department of Computing, Imperial College London, London, UK（计算系，伦敦帝国学院，英国伦敦）；Deutsches Konsortium für Translationale Krebsforschung (DKTK), Partner Site Munich, Munich, Germany（德国转化癌症研究联盟（DKTK），慕尼黑分部，德国慕尼黑）
Agentic and Generative AI for Open-Source Intelligence and Cyber Investigations: Taxonomy, Evaluation, Challenges, and Future Directions
School of Computer Science and Mathematics, Keele University（基尔大学计算机科学与数学学院）；Cybersecurity Institute, University of Liverpool（利物浦大学网络安全研究所）；Department of Applied Computing IICL, University of Wales Trinity Saint David（威尔士特里尼达大学应用计算系）；Deakin Cyber Research and Innovation Hub, Deakin University（德金大学网络安全研究与创新中心）；Department of Information Systems and Cyber Security, The University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校信息系与网络安全系）
