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应对无形风险:在人工智能时代保障医学成像安全

Navigating the unseen peril: safeguarding medical imaging in the age of AI.

作者信息

Maertens Alexandra, Brykman Steve, Hartung Thomas, Gafita Andrei, Bai Harrison, Hoelzer David, Skoudis Ed, Paller Channing Judith

机构信息

Center for Alternatives to Animal Testing (CAAT), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.

Independent Creative Technologist, Boston, MA, United States.

出版信息

Front Artif Intell. 2024 Dec 9;7:1400732. doi: 10.3389/frai.2024.1400732. eCollection 2024.

Abstract

In response to the increasing significance of artificial intelligence (AI) in healthcare, there has been increased attention - including a Presidential executive order to create an AI Safety Institute - to the potential threats posed by AI. While much attention has been given to the conventional risks AI poses to cybersecurity, and critical infrastructure, here we provide an overview of some unique challenges of AI for the medical community. Above and beyond obvious concerns about vetting algorithms that impact patient care, there are additional subtle yet equally important things to consider: the potential harm AI poses to its own integrity and the broader medical information ecosystem. Recognizing the role of healthcare professionals as both consumers and contributors to AI training data, this article advocates for a proactive approach in understanding and shaping the data that underpins AI systems, emphasizing the need for informed engagement to maximize the benefits of AI while mitigating the risks.

摘要

随着人工智能(AI)在医疗保健领域的重要性日益增加,人们对AI带来的潜在威胁给予了更多关注,包括总统发布的创建AI安全研究所的行政命令。虽然人们对AI给网络安全和关键基础设施带来的传统风险给予了很多关注,但在此我们概述一下AI给医学界带来的一些独特挑战。除了对影响患者护理的算法审查的明显担忧之外,还有其他一些微妙但同样重要的事情需要考虑:AI对其自身完整性和更广泛的医疗信息生态系统可能造成的危害。认识到医疗保健专业人员既是AI训练数据的消费者又是贡献者,本文提倡采取积极主动的方法来理解和塑造支撑AI系统的数据,强调需要进行明智的参与,以在降低风险的同时最大化AI的益处。

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How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?剪枝如何影响长尾多标签医学图像分类器?
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