Despite the success of Convolutional Neural Network-based Computer-Aided Diagnosis research, its clinical applications remain challenging. Accordingly, developing medical Artificial Intelligence (AI) fitting into a clinical environment requires identifying/bridging the gap between AI and Healthcare sides. Since the biggest problem in Medical Imaging lies in data paucity, confirming the clinical relevance for diagnosis of research-proven image augmentation techniques is essential. Therefore, we hold a clinically valuable AI-envisioning workshop among Japanese Medical Imaging experts, physicians, and generalists in Healthcare/Informatics. Then, a questionnaire survey for physicians evaluates our pathology-aware Generative Adversarial Network (GAN)-based image augmentation projects in terms of Data Augmentation and physician training. The workshop reveals the intrinsic gap between AI/Healthcare sides and solutions on Why (i.e., clinical significance/interpretation) and How (i.e., data acquisition, commercial deployment, and safety/feeling safe). This analysis confirms our pathology-aware GANs' clinical relevance as a clinical decision support system and non-expert physician training tool. Our findings would play a key role in connecting inter-disciplinary research and clinical applications, not limited to the Japanese medical context and pathology-aware GANs.
Medical world models in healthcare: foundations, applications, and challenges for trustworthy clinical translation
National and Local Joint Engineering Laboratory of Computer Aided Design, School of Software Engineering, Dalian University（大连大学软件工程学院计算机辅助设计国家地方联合工程实验室）；Department of Radiology, Xinhua Hospital Affiliated to Dalian University（大连大学附属新华医院放射科）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of California, San Francisco（加州大学旧金山分校）；Yale University（耶鲁大学）；The Hong Kong Polytechnic University（香港理工大学）
University of Minnesota（明尼苏达大学）；University of Pittsburgh（匹兹堡大学）
Electronic Health Records (EHR) data analysis plays a crucial role in healthcare system quality. Because of its highly complex underlying causality and limited observable nature, causal inference on EHR is quite challenging. Deep Learning (DL) achieved great success among the advanced machine learning methodologies. Nevertheless, it is still obstructed by the inappropriately assumed causal conditions. This work proposed a novel method to quantify clinically well-defined causal effects as a generalized estimation vector that is simply utilizable for causal models. We incorporated it into DL mod…
Artificial Intelligence in Rural Healthcare Delivery: Bridging Gaps and Enhancing Equity through Innovation
University of Illinois College of Medicine Rockford（伊利诺伊大学罗克福德医学院）；National Center for Rural Health Professions（农村健康专业中心）；Sri Sivasubramaniya Nadar College of Engineering（塞维萨布拉曼尼亚那德尔工程学院）；M. Kumarasamy College of Engineering（M. Kumarasamy 工程学院）；The Ohio State University Wexner Medical Center（俄亥俄州立大学韦克斯纳医疗中心）；iWorks Corporation（iWorks 公司）；Central South University（中南大学）；Bacha Khan University（巴哈汗大学）
University of Maryland School of Medicine（马里兰大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease. In real-world clinical use, multiple diseases need to be considered since they can co-exist in the same patient. In this work, we demonstrate how federated learning (FL) can be used to make these toy CXR datasets from Kaggle clinically useful. Specifically, we train a single FL classification model (`global`) using two separate CXR datasets -- one annotated for presence of pneumonia and the …
Bridging Brain Connectomes and Clinical Reports for Early Alzheimer's Disease Diagnosis
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Cambridge（剑桥大学）；University of Sheffield（谢菲尔德大学）；The University of Georgia（佐治亚大学）
NeuroBridge: Bridging Multi-Task MRI Knowledge for Neurodegenerative Disease Diagnosis
Department of Mechanical Engineering, Boston University（波士顿大学机械工程系）；The Photonics Center, Boston University（波士顿大学光子中心）；Rafik B. Hariri Institute for Computing and Computational Science & Engineering, Boston University（波士顿大学拉菲克·B·哈里里计算与计算科学与工程研究所）；Department of Radiology, Boston University Chobanian & Avedisian School of Medicine（波士顿大学切博尼亚与阿维迪亚安医学学院放射科）；Department of Radiology, Boston Medical Center（波士顿医学中心放射科）；Department of Electrical and Computer Engineering, Boston University（波士顿大学电气与计算机工程系）；Department of Biomedical Engineering, Boston University（波士顿大学生物医学工程系）；Division of Materials Science and Engineering, Boston University（波士顿大学材料科学与工程系）
Roux Institute（鲁克斯研究所）；Northeastern University（东北大学）；University of Maine（缅因大学）
We lay the groundwork for research in the algorithmic comprehension of infant faces, in anticipation of applications from healthcare to psychology, especially in the early prediction of developmental disorders. Specifically, we introduce the first-ever dataset of infant faces annotated with facial landmark coordinates and pose attributes, demonstrate the inadequacies of existing facial landmark estimation algorithms in the infant domain, and train new state-of-the-art models that significantly improve upon those algorithms using domain adaptation techniques. We touch on the closely related tas…
A Causal Framework for Mitigating Data Shifts in Healthcare
Causality in Healthcare AI Hub (CHAI)（医疗人工智能AI hub（CHAI））；The University of Edinburgh（爱丁堡大学）；University of Manchester（曼彻斯特大学）；Athena Research Center（阿塔尼亚研究中心）；NKU Athens（雅典NKU）；King’s College London（伦敦国王学院）；Imperial College London（伦敦帝国学院）；University of Exeter（埃克塞特大学）；University College London（伦敦大学学院）
Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in health…
Bridging visual saliency and large language models for explainable deep learning in medical imaging
Department of Mathematics and Computer science（数学与计算机科学系）；Institute of Cognitive Science（认知科学研究所）；Rhodes University（罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS)（理论与计算科学国家研究所）
MedGround: Bridging the Evidence Gap in Medical Vision-Language Models with Verified Grounding Data
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；DAMO Academy, Alibaba Group（阿里巴巴集团 DAMO 院）；Hupan Lab, Zhejiang Province（浙江省 Hupan 实验室）
From Visual Attribution to Clinical Reasoning: Explainable Parkinson's Disease Screening from Hand-Drawn Patterns
Indian Institute of Technology Indore（印度理工学院印多尔分校）；Indian Institute of Technology Patna（印度理工学院巴特那分校）；M. R. Bangur Hospital（M. R. 班古尔医院）；University of Western Australia（西澳大利亚大学）
Towards Scalable SOAP Note Generation: A Weakly Supervised Multimodal Framework
Department of Computer Science, University of Maryland, Baltimore County（计算机科学系，马里兰大学巴尔的摩县分校）；Department of Dermatology, Johns Hopkins University School of Medicine（皮肤科系，约翰霍普金斯大学医学院）
Bridging the Markerless Tracking Validation Gap: Intraoperative X-ray Workflow Integration
The Hamlyn Centre for Robotic Surgery, Imperial College London（伦敦帝国理工学院哈姆林外科机器人中心）；Imperial College Healthcare NHS Trust（帝国理工学院医疗保健 NHS 信托基金）；Stanford University School of Medicine（斯坦福大学医学院）
TCM-Tongue: A Standardized Tongue Image Dataset with Pathological Annotations for AI-Assisted TCM Diagnosis
Beijing Technology and Business University（北京工商大学）；Xiyuan Hospital of China Academy of Chinese Medical Sciences（中国中医科学院西苑医院）；Peking University People's Hospital（北京大学人民医院）
Skin-SOAP: A Weakly Supervised Framework for Generating Structured SOAP Notes
Department of Computer Science, University of Maryland, Baltimore County（计算机科学系，马里兰大学巴尔的摩县分校）；Department of Dermatology, Johns Hopkins University School of Medicine（皮肤科系，约翰霍普金斯大学医学院）
Bias-constrained multimodal intelligence for equitable and reliable clinical AI
Paul C. Lauterbur Research Center for Biomedical Imaging（Paul C. Lauterbur生物医学成像研究中心）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（深圳先进技术研究院，中国科学院）；Pengcheng Laboratory（鹏城实验室）；University of Chinese Academy of Sciences（中国科学院大学）；Department of Radiology, Beijing Chaoyang Hospital, Capital Medical University（首都医科大学北京朝阳医院放射科）；Department of Urology, South China Hospital, Medical School, Shenzhen University（深圳大学南方医院泌尿科）；Huawei Technologies Co., Ltd.（华为技术有限公司）
PASSION for Dermatology: Bridging the Diversity Gap with Pigmented Skin Images from Sub-Saharan Africa
University Hospital Basel（巴塞尔大学医院）；University of Basel（巴塞尔大学）；Lucerne University of Applied Sciences and Arts（卢塞恩应用科学与艺术大学）；Laboratoire d’Accueil et de Recherche en santé publique spécialisé en TIC（公共卫生接待与研究实验室（专注于信息通信技术））；Regional Dermatology Training Center（地区皮肤病培训中心）；Kamuzu University of Health Sciences（卡穆祖卫生科学大学）；Bwaila Hospital（布瓦拉医院）；Clinique dermatologique（皮肤病诊所）
