文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

人工智能应用和共享临床影像数据的伦理问题:一个建议框架。

Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework.

机构信息

From the Department of Radiology, Stanford University School of Medicine, 300 Pasteur Dr, Stanford, CA 94305-5105.

出版信息

Radiology. 2020 Jun;295(3):675-682. doi: 10.1148/radiol.2020192536. Epub 2020 Mar 24.


DOI:10.1148/radiol.2020192536
PMID:32208097
Abstract

In this article, the authors propose an ethical framework for using and sharing clinical data for the development of artificial intelligence (AI) applications. The philosophical premise is as follows: when clinical data are used to provide care, the primary purpose for acquiring the data is fulfilled. At that point, clinical data should be treated as a form of public good, to be used for the benefit of future patients. In their 2013 article, Faden et al argued that all who participate in the health care system, including patients, have a moral obligation to contribute to improving that system. The authors extend that framework to questions surrounding the secondary use of clinical data for AI applications. Specifically, the authors propose that all individuals and entities with access to clinical data become data stewards, with fiduciary (or trust) responsibilities to patients to carefully safeguard patient privacy, and to the public to ensure that the data are made widely available for the development of knowledge and tools to benefit future patients. According to this framework, the authors maintain that it is unethical for providers to "sell" clinical data to other parties by granting access to clinical data, especially under exclusive arrangements, in exchange for monetary or in-kind payments that exceed costs. The authors also propose that patient consent is not required before the data are used for secondary purposes when obtaining such consent is prohibitively costly or burdensome, as long as mechanisms are in place to ensure that ethical standards are strictly followed. Rather than debate whether patients or provider organizations "own" the data, the authors propose that clinical data are not owned at all in the traditional sense, but rather that all who interact with or control the data have an obligation to ensure that the data are used for the benefit of future patients and society.

摘要

在本文中,作者提出了一个使用和共享临床数据以开发人工智能 (AI) 应用程序的伦理框架。其哲学前提如下:当临床数据被用于提供护理时,获取数据的主要目的就已经达到了。此时,临床数据应被视为一种公共利益,用于造福未来的患者。在他们 2013 年的文章中,Faden 等人认为,所有参与医疗保健系统的人,包括患者,都有义务为改善该系统做出贡献。作者将该框架扩展到围绕临床数据用于 AI 应用的二次使用的问题。具体来说,作者提出,所有可以访问临床数据的个人和实体都应成为数据管理者,对患者负有信托责任,谨慎保护患者隐私,并对公众负责,确保数据广泛用于开发知识和工具,以造福未来的患者。根据该框架,作者认为,提供者通过授予临床数据访问权限将数据“出售”给其他方是不道德的,尤其是在独家安排下,以超出成本的金钱或实物支付作为交换。作者还提出,只要建立了确保严格遵守道德标准的机制,在获得同意过于昂贵或繁琐而无法获得同意的情况下,无需在二次使用数据之前征得患者同意。作者建议,不要争论患者或提供者组织“拥有”数据,而是建议临床数据在传统意义上根本不属于任何人所有,而是所有与数据交互或控制数据的人都有义务确保数据用于造福未来的患者和社会。

相似文献

[1]
Ethics of Using and Sharing Clinical Imaging Data for Artificial Intelligence: A Proposed Framework.

Radiology. 2020-3-24

[2]
Ethical Use of Electronic Health Record Data and Artificial Intelligence: Recommendations of the Primary Care Informatics Working Group of the International Medical Informatics Association.

Yearb Med Inform. 2020-8

[3]
Canadian Association of Radiologists White Paper on De-identification of Medical Imaging: Part 2, Practical Considerations.

Can Assoc Radiol J. 2021-2

[4]
A governance model for the application of AI in health care.

J Am Med Inform Assoc. 2020-3-1

[5]
Canadian Association of Radiologists White Paper on De-Identification of Medical Imaging: Part 1, General Principles.

Can Assoc Radiol J. 2021-2

[6]
Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology.

Can Assoc Radiol J. 2019-4-5

[7]
The patient experience of patient-centered communication with nurses in the hospital setting: a qualitative systematic review protocol.

JBI Database System Rev Implement Rep. 2015-1

[8]
Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey.

J Med Internet Res. 2021-8-26

[9]
Ethical Considerations in the Advent of Artificial Intelligence in Otolaryngology.

Otolaryngol Head Neck Surg. 2019-11-26

[10]
Your Robot Therapist Will See You Now: Ethical Implications of Embodied Artificial Intelligence in Psychiatry, Psychology, and Psychotherapy.

J Med Internet Res. 2019-5-9

引用本文的文献

[1]
Deep Learning for the Early Detection of Invasive Ductal Carcinoma in Histopathological Images: Convolutional Neural Network Approach With Transfer Learning.

JMIR Form Res. 2025-8-21

[2]
Sharing a whole-/total-body [18F]FDG-PET/CT dataset with CT-derived segmentations: an ENHANCE.PET initiative.

Res Sq. 2025-8-5

[3]
Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment.

Diagnostics (Basel). 2025-6-27

[4]
Exploring Opportunities and Challenges of AI in Primary Healthcare: A Qualitative Study with Family Doctors in Lithuania.

Healthcare (Basel). 2025-6-14

[5]
Artificial intelligence in medical imaging diagnosis: are we ready for its clinical implementation?

J Med Imaging (Bellingham). 2025-11

[6]
AI-Derived Blood Biomarkers for Ovarian Cancer Diagnosis: Systematic Review and Meta-Analysis.

J Med Internet Res. 2025-3-24

[7]
The application of artificial intelligence in insomnia, anxiety, and depression: A bibliometric analysis.

Digit Health. 2025-3-2

[8]
Addressing biomedical data challenges and opportunities to inform a large-scale data lifecycle for enhanced data sharing, interoperability, analysis, and collaboration across stakeholders.

Sci Rep. 2025-2-21

[9]
From Serendipity to Precision: Integrating AI, Multi-Omics, and Human-Specific Models for Personalized Neuropsychiatric Care.

Biomedicines. 2025-1-12

[10]
Developing a Research Center for Artificial Intelligence in Medicine.

Mayo Clin Proc Digit Health. 2024-12

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索