Developing, Purchasing, Implementing and Monitoring AI Tools in Radiology: Practical Considerations. A Multi-Society Statement from the ACR, CAR, ESR, RANZCR and RSNA.

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Adrian P Brady, Bibb Allen, Jaron Chong, Elmar Kotter, Nina Kottler, John Mongan, Lauren Oakden-Rayner, Daniel Pinto Dos Santos, An Tang, Christoph Wald, John Slavotinek
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引用次数: 0

Abstract

Artificial Intelligence (AI) carries the potential for unprecedented disruption in radiology, with possible positive and negative consequences. The integration of AI in radiology holds the potential to revolutionize healthcare practices by advancing diagnosis, quantification, and management of multiple medical conditions. Nevertheless, the ever-growing availability of AI tools in radiology highlights an increasing need to critically evaluate claims for its utility and to differentiate safe product offerings from potentially harmful, or fundamentally unhelpful ones. This multi-society paper, presenting the views of Radiology Societies in the USA, Canada, Europe, Australia, and New Zealand, defines the potential practical problems and ethical issues surrounding the incorporation of AI into radiological practice. In addition to delineating the main points of concern that developers, regulators, and purchasers of AI tools should consider prior to their introduction into clinical practice, this statement also suggests methods to monitor their stability and safety in clinical use, and their suitability for possible autonomous function. This statement is intended to serve as a useful summary of the practical issues which should be considered by all parties involved in the development of radiology AI resources, and their implementation as clinical tools. This article is simultaneously published in Insights into Imaging (DOI 10.1186/s13244-023-01541-3), Journal of Medical Imaging and Radiation Oncology (DOI 10.1111/1754-9485.13612), Canadian Association of Radiologists Journal (DOI 10.1177/08465371231222229), Journal of the American College of Radiology (DOI 10.1016/j.jacr.2023.12.005), and Radiology: Artificial Intelligence (DOI 10.1148/ryai.230513). Keywords: Artificial Intelligence, Radiology, Automation, Machine Learning Published under a CC BY 4.0 license. ©The Author(s) 2024. Editor's Note: The RSNA Board of Directors has endorsed this article. It has not undergone review or editing by this journal.

开发、采购、实施和监控放射学中的人工智能工具:实用考虑因素。来自 ACR、CAR、ESR、RANZCR 和 RSNA 的多协会声明。
人工智能(AI)有可能对放射学造成前所未有的破坏,并可能带来积极和消极的影响。将人工智能整合到放射学中,有可能推进多种医疗状况的诊断、量化和管理,从而彻底改变医疗实践。然而,放射学中的人工智能工具越来越多,这凸显出越来越有必要对其实用性进行严格评估,并将安全的产品与可能有害或根本无益的产品区分开来。这篇由多个学会共同撰写的论文阐述了美国、加拿大、欧洲、澳大利亚和新西兰放射学会的观点,明确了将人工智能应用于放射实践的潜在实际问题和伦理问题。除了阐述人工智能工具的开发者、监管者和购买者在将其引入临床实践之前应考虑的主要关注点之外,本声明还提出了监测其在临床使用中的稳定性和安全性以及是否适合发挥自主功能的方法。本声明旨在对参与放射学人工智能资源开发及其作为临床工具实施的各方应考虑的实际问题进行有益的总结。本文同时发表于《Insights into Imaging》(DOI 10.1186/s13244-023-01541-3)、《Journal of Medical Imaging and Radiation Oncology》(DOI 10.1111/1754-9485.13612)、《Canadian Association of Radiologists Journal》(DOI 10.1177/08465371231222229)、《Journal of the American College of Radiology》(DOI 10.1016/j.jacr.2023.12.005)和《Radiology:人工智能》(DOI 10.1148/ryai.230513)。关键词:人工智能人工智能 放射学 自动化 机器学习 采用 CC BY 4.0 许可发布。©作者 2024。编者注:RSNA 董事会已认可本文。本文未经本刊审阅或编辑。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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