Clinical acceptance of software based on artificial intelligence technologies (radiology)
MOSCOW HEALTH CARE DEPARTMENT RESEARCH AND PRACTICAL CLINICAL CENTER FOR DIAGNOSTICS AND TELEMEDICINE TECHNOLOGIES（莫斯科卫生部诊断与远程医疗技术研究与实践临床中心）；MOSCOW HEALTH CARE DEPARTMENT BEST PRACTICES IN MEDICAL IMAGING（莫斯科卫生部医学影像最佳实践中心）
S. P. Morozov, A. V. Vladzymyrskyy, V. G. Klyashtornyy, A. E. Andreychenko, N. S. Kulberg, V. A. Gombolevsky, K. A. Sergunova
备注：
For correspondence: info@npcmr.ru, npcmr@zdrav.mos.ru. 28/1, Srednyaya Kalitnikovskaya st., Moscow, 109029, Russia +7 (495) 276-04-36
Aim: provide a methodological framework for the process of clinical tests, clinical acceptance, and scientific assessment of algorithms and software based on the artificial intelligence (AI) technologies. Clinical tests are considered as a preparation stage for the software registration as a medical product. The authors propose approaches to evaluate accuracy and efficiency of the AI algorithms for radiology.
CLEAR: A Clinically-Grounded Tabular Framework for Radiology Report Evaluation
University of Chicago（芝加哥大学）；Tsinghua University（清华大学）；Zhejiang University（浙江大学）；University of Michigan Department of Statistics（密歇根大学统计学系）；University of Chicago Department of Computer Science（芝加哥大学计算机科学系）；University of Chicago Department of Computer Science and Technology（芝加哥大学计算机科学与技术系）；Tsinghua University Department of Radiology（清华大学放射学系）；University of Chicago College of Control Science and Engineering（芝加哥大学控制科学与工程学院）
Extracting structured clinical information from free-text radiology reports can enable the use of radiology report information for a variety of critical healthcare applications. In our work, we present RadGraph, a dataset of entities and relations in full-text chest X-ray radiology reports based on a novel information extraction schema we designed to structure radiology reports. We release a development dataset, which contains board-certified radiologist annotations for 500 radiology reports from the MIMIC-CXR dataset (14,579 entities and 10,889 relations), and a test dataset, which contains t…
Clinically Grounded Agent-based Report Evaluation: An Interpretable Metric for Radiology Report Generation
New York University（纽约大学）；NYU Langone Health（纽约大学兰格医学中心）；Genentech（基因泰克）；NYU Grossman School of Medicine（纽约大学格罗斯曼医学院）；Wenzhou Medical University（温州医科大学）；Macau University of Science and Technology（澳门科学大学）
RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores
School of Electrical and Computer Engineering, University of Sydney（悉尼大学电气与计算机工程学院）；School of Computer Science, University of Adelaide（阿德莱德大学计算机科学学院）；School of Computing and Information Technology, University of Wollongong（沃林根大学计算与信息科技学院）
Medical AI Consensus: A Multi-Agent Framework for Radiology Report Generation and Evaluation
Graduate School of Information Science, University of Hyogo（京都大学垣田学园信息科学研究生院）；Center for Informatics Science, School of Information Technology and Computer Science, Nile University（尼罗大学信息科学中心）
Dual-Modal Lung Cancer AI: Interpretable Radiology and Microscopy with Clinical Risk Integration
Hatyaiwittayalai School（哈雅维泰莱学校）；School of Information, Computer, and Communication Technology（信息、计算机与通信技术学院）；Sirindhorn International Institute of Technology（Sirindhorn国际技术学院）；Thammasat University（泰国朱拉大学）；Pathum Thani, Thailand（帕亨他尼，泰国）
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Northwestern Polytechnical University（西北工业大学）；The Second Xiangya Hospital, Central South University（中南大学湘雅二医院）；Clinical Research Center for Medical Imaging in Hunan Province（湖南省医学影像临床研究中心）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Institute of Medical Research, Northwestern Polytechnical University（西北工业大学医学研究院）；Glasgow College, University of Electronic Science and Technology of China（电子科技大学格拉斯哥学院）；Central South University（中南大学）
CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔兰大学）；University of Oldenburg（奥尔登堡大学）；Dhaka Medical College Hospital（达卡医学院医院）
Volumetric Radiology AI in the Era of Multimodal Large Language Models
Fudan University（复旦大学）；Northwestern Polytechnical University（西北工业大学）；Xi’an Jiaotong University（西安交通大学）；Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；Southern Medical University（南方医科大学）；The Chinese University of Hong Kong（香港中文大学）；University of British Columbia（不列颠哥伦比亚大学）；Microsoft Research（微软研究院）；Westlake University（西湖大学）
University of Rochester（罗切斯特大学）；Ludwig Maximilian University（路德维希·马克西米利安大学）
There is an urgent need for streamlining radiology Quality Assurance (QA) programs to make them better and faster. Here, we present a novel approach, Artificial Intelligence (AI)-Based QUality Assurance by Restricted Investigation of Unequal Scores (AQUARIUS), for re-defining radiology QA, which reduces human effort by up to several orders of magnitude over existing approaches. AQUARIUS typically includes automatic comparison of AI-based image analysis with natural language processing (NLP) on radiology reports. Only the usually small subset of cases with discordant reads is subsequently revie…
Best Practices for Large Language Models in Radiology
Stanford Center for Artificial Intelligence in Medicine and Imaging（斯坦福大学人工智能医学与成像中心）；Diagnostic and Interventional Radiology, University Hospital Zurich, University of Zurich（苏黎世大学医院放射诊断与介入放射学）；Department of Electrical Engineering, Stanford University（斯坦福大学电气工程系）；Department of Cardiothoracic Surgery, Stanford Medicine（斯坦福医学院心胸外科系）；Hugging Face；Department of Medical Education, Icahn School of Medicine at Mount Sinai（伊坎医学院Mount Sinai医学教育系）；NVIDIA Corporation（NVIDIA公司）；UT Health San Antonio（UT健康科学中心圣安东尼奥分校）；Department of Dermatology, Redwood City, CA, USA（红木城加州大学皮肤病学系）；Department of Biomedical Data Science, Stanford, CA, USA（斯坦福大学生物医学数据科学系）；Department of Medicine, Stanford, CA, USA（斯坦福大学医学系）；Department of Radiology, Stanford University（斯坦福大学放射学系）
ReportMedSAM: Guiding Segmentation Through Radiology Reports
School of Engineering, College of Engineering and Physical Sciences, University of Birmingham（工程学院，工程与物理科学学院，伯明翰大学）；Department of Cancer and Genomic Sciences, College of Medicine and Health, University of Birmingham（癌症与基因组科学系，医学与健康学院，伯明翰大学）；Department of Computer Science, Faculty of Science and Engineering, University of Manchester（计算机科学系，科学与工程学院，曼彻斯特大学）；The Division of Informatics, Imaging and Data Science, Faculty of Biology, Medicine, and Health, University of Manchester（信息学、成像与数据科学部，生物、医学与健康学院，曼彻斯特大学）
Human-AI Interaction (HAIx) Lab, Indian Institute of Technology Gandhinagar（人类-人工智能交互实验室，印度理工学院冈丁加尔）；Department of Computer Science and Engineering, Madhav Institute of Technology and Science Deemed University (MITS-DU)（计算机科学与工程系，马达夫技术与科学 deemed 大学（MITS-DU））；Multimedia and Information Security Research Group, Department of Computer Science and Engineering, ABV-Indian Institute of Information Technology and Management（多媒体与信息安全研究组，计算机科学与工程系，ABV-印度信息科技与管理学院）
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Children’s Hospital of Philadelphia（费城儿童医院）；Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）；National Cancer Institute（美国国家癌症研究所）；Children's National Hospital（儿童国家医院）；Sheikh Zayed Institute for Pediatric Surgical Innovation（谢赫扎耶德儿科外科创新研究院）
The Era of Foundation Models in Medical Imaging is Approaching : A Scoping Review of the Clinical Value of Large-Scale Generative AI Applications in Radiology
Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for Radiology Reports
National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health（美国国立卫生研究院国家医学图书馆国家生物技术信息中心）；National Institutes of Health Clinical Center（美国国立卫生研究院临床中心）
Shiraz University（设拉子大学）；Shiraz University of Medical Sciences（设拉子医科大学）
Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN)…
Seeing Through Multiple Views: Parameter-Efficient Fine-Tuning via Selective Neurons for Consistent Radiology Report Generation
MedVisAI Lab（MedVisAI实验室）；Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore（新加坡南洋理工大学李光前医学院）；Centre of AI in Medicine, Singapore（新加坡人工智能医学中心）；The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong（香港中文大学）；National Heart Centre Singapore (NHCS), Singapore（新加坡国家心脏中心）
