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基于人工智能的胸部X光解读检测辅助设备的临床应用:现状与实际考量

[Clinical Application of Artificial Intelligence-Based Detection Assistance Devices for Chest X-Ray Interpretation: Current Status and Practical Considerations].

作者信息

Hwang Eui Jin

出版信息

J Korean Soc Radiol. 2024 Jul;85(4):693-704. doi: 10.3348/jksr.2024.0052. Epub 2024 Jul 25.

DOI:10.3348/jksr.2024.0052
PMID:39130790
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11310435/
Abstract

Artificial intelligence (AI) technology is actively being applied for the interpretation of medical imaging, such as chest X-rays. AI-based software medical devices, which automatically detect various types of abnormal findings in chest X-ray images to assist physicians in their interpretation, are actively being commercialized and clinically implemented in Korea. Several important issues need to be considered for AI-based detection assistant tools to be applied in clinical practice: the evaluation of performance and efficacy prior to implementation; the determination of the target application, range, and method of delivering results; and monitoring after implementation and legal liability issues. Appropriate decision making regarding these devices based on the situation in each institution is necessary. Radiologists must be engaged as medical assessment experts using the software for these devices as well as in medical image interpretation to ensure the safe and efficient implementation and operation of AI-based detection assistant tools.

摘要

人工智能(AI)技术正积极应用于医学影像解读,如胸部X光。基于AI的软件医疗设备可自动检测胸部X光图像中的各类异常发现,以协助医生进行解读,此类设备正在韩国积极商业化并应用于临床。要将基于AI的检测辅助工具应用于临床实践,需要考虑几个重要问题:实施前的性能和功效评估;目标应用、结果传递范围和方法的确定;实施后的监测以及法律责任问题。根据各机构的情况对这些设备做出适当决策很有必要。放射科医生必须作为医学评估专家参与使用这些设备的软件以及医学影像解读,以确保基于AI的检测辅助工具安全、高效地实施和运行。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7403/11310435/4a4a35c9cef7/jksr-85-693-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7403/11310435/931edd747e82/jksr-85-693-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7403/11310435/4a4a35c9cef7/jksr-85-693-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7403/11310435/931edd747e82/jksr-85-693-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7403/11310435/4a4a35c9cef7/jksr-85-693-g002.jpg

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本文引用的文献

1
2023 Survey on User Experience of Artificial Intelligence Software in Radiology by the Korean Society of Radiology.2023 年韩国放射学会对人工智能软件在放射学中的用户体验的调查。
Korean J Radiol. 2024 Jul;25(7):613-622. doi: 10.3348/kjr.2023.1246.
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了解使用医疗人工智能工具的责任风险。
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The Staffing Crisis and Burnout in Academic Radiology: Insights from a Survey Study in Korea.韩国一项调查研究揭示:学术放射科人员配备危机与倦怠现象
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Artificial Intelligence for Assessment of Endotracheal Tube Position on Chest Radiographs: Validation in Patients From Two Institutions.人工智能在胸部 X 光片上评估气管插管位置中的应用:来自两个机构的患者的验证。
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Diagnostic accuracy of three computer-aided detection systems for detecting pulmonary tuberculosis on chest radiography when used for screening: Analysis of an international, multicenter migrants screening study.三种计算机辅助检测系统用于胸部X线筛查肺结核的诊断准确性:一项国际多中心移民筛查研究的分析
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