在放射学中部署人工智能的临床、文化、计算和监管考虑因素:RSNA 和 MICCAI 专家的观点。

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Marius George Linguraru, Spyridon Bakas, Mariam Aboian, Peter D Chang, Adam E Flanders, Jayashree Kalpathy-Cramer, Felipe C Kitamura, Matthew P Lungren, John Mongan, Luciano M Prevedello, Ronald M Summers, Carol C Wu, Maruf Adewole, Charles E Kahn
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引用次数: 0

摘要

"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现一些可能影响内容的错误。北美放射学会(RSNA)和医学影像计算与计算机辅助介入学会(MICCAI)联合举办了一系列专题讨论会和研讨会,重点探讨人工智能(AI)在放射学领域的当前影响和未来发展方向。这些对话收集了来自放射学、医学影像和机器学习等多学科专家的观点,探讨了人工智能技术目前在放射学中的临床应用,以及它如何受到信任、可重复性、可解释性和问责制的影响。这些观点从实践和哲学角度共同定义了放射科医生和人工智能科学家合作的文化变革,并描述了人工智能技术要获得广泛认可所面临的挑战。本文介绍了来自 MICCAI 和 RSNA 的专家对临床、文化、计算和监管方面的考虑因素的观点,以及推荐的阅读材料,这些因素对于在放射学和更广泛的临床实践中成功采用人工智能技术至关重要。该报告强调了合作对于改进临床部署的重要性,强调了整合临床和医学影像数据的必要性,并介绍了确保顺利整合和激励整合的策略。©RSNA,2024。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Clinical, Cultural, Computational, and Regulatory Considerations to Deploy AI in Radiology: Perspectives of RSNA and MICCAI Experts.

The Radiological Society of North of America (RSNA) and the Medical Image Computing and Computer Assisted Intervention (MICCAI) Society have led a series of joint panels and seminars focused on the present impact and future directions of artificial intelligence (AI) in radiology. These conversations have collected viewpoints from multidisciplinary experts in radiology, medical imaging, and machine learning on the current clinical penetration of AI technology in radiology and how it is impacted by trust, reproducibility, explainability, and accountability. The collective points-both practical and philosophical-define the cultural changes for radiologists and AI scientists working together and describe the challenges ahead for AI technologies to meet broad approval. This article presents the perspectives of experts from MICCAI and RSNA on the clinical, cultural, computational, and regulatory considerations-coupled with recommended reading materials-essential to adopt AI technology successfully in radiology and, more generally, in clinical practice. The report emphasizes the importance of collaboration to improve clinical deployment, highlights the need to integrate clinical and medical imaging data, and introduces strategies to ensure smooth and incentivized integration. Keywords: Adults and Pediatrics, Computer Applications-General (Informatics), Diagnosis, Prognosis © RSNA, 2024.

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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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