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本文引用的文献

1
Latent bias and the implementation of artificial intelligence in medicine.医学人工智能应用中的潜在偏见
J Am Med Inform Assoc. 2020 Dec 9;27(12):2020-2023. doi: 10.1093/jamia/ocaa094.
2
Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.使用真实世界电子健康记录数据的可解释人工智能模型:系统范围界定综述。
J Am Med Inform Assoc. 2020 Jul 1;27(7):1173-1185. doi: 10.1093/jamia/ocaa053.
3
Artificial intelligence versus clinicians: systematic review of design, reporting standards, and claims of deep learning studies.人工智能与临床医生:深度学习研究的设计、报告标准和主张的系统评价。
BMJ. 2020 Mar 25;368:m689. doi: 10.1136/bmj.m689.
4
The Ethics of Medical AI and the Physician-Patient Relationship.医疗 AI 的伦理与医患关系
Camb Q Healthc Ethics. 2020 Jan;29(1):115-121. doi: 10.1017/S0963180119000847.
5
On the ethics of algorithmic decision-making in healthcare.论医疗保健中算法决策的伦理问题。
J Med Ethics. 2020 Mar;46(3):205-211. doi: 10.1136/medethics-2019-105586. Epub 2019 Nov 20.
6
A governance model for the application of AI in health care.人工智能在医疗保健领域应用的治理模型。
J Am Med Inform Assoc. 2020 Mar 1;27(3):491-497. doi: 10.1093/jamia/ocz192.
7
Key challenges for delivering clinical impact with artificial intelligence.人工智能实现临床影响的关键挑战。
BMC Med. 2019 Oct 29;17(1):195. doi: 10.1186/s12916-019-1426-2.
8
The right to refuse diagnostics and treatment planning by artificial intelligence.拒绝人工智能进行诊断和治疗规划的权利。
Med Health Care Philos. 2020 Mar;23(1):107-114. doi: 10.1007/s11019-019-09912-8.
9
What should medical students know about artificial intelligence in medicine?医学生应该了解关于医学人工智能的哪些方面?
J Educ Eval Health Prof. 2019;16:18. doi: 10.3352/jeehp.2019.16.18. Epub 2019 Jul 3.
10
With an eye to AI and autonomous diagnosis.着眼于人工智能与自主诊断。
NPJ Digit Med. 2018 Aug 28;1:40. doi: 10.1038/s41746-018-0048-y. eCollection 2018.

信任与医疗 AI:我们面临的挑战和克服这些挑战所需的专业知识。

Trust and medical AI: the challenges we face and the expertise needed to overcome them.

机构信息

Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia.

Centre for AI and Digital Ethics, School of Computing and Information Systems, University of Melbourne, Melbourne, Australia.

出版信息

J Am Med Inform Assoc. 2021 Mar 18;28(4):890-894. doi: 10.1093/jamia/ocaa268.

DOI:10.1093/jamia/ocaa268
PMID:33340404
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7973477/
Abstract

Artificial intelligence (AI) is increasingly of tremendous interest in the medical field. How-ever, failures of medical AI could have serious consequences for both clinical outcomes and the patient experience. These consequences could erode public trust in AI, which could in turn undermine trust in our healthcare institutions. This article makes 2 contributions. First, it describes the major conceptual, technical, and humanistic challenges in medical AI. Second, it proposes a solution that hinges on the education and accreditation of new expert groups who specialize in the development, verification, and operation of medical AI technologies. These groups will be required to maintain trust in our healthcare institutions.

摘要

人工智能(AI)在医学领域越来越受到关注。然而,医疗 AI 的失败可能会对临床结果和患者体验产生严重后果。这些后果可能会削弱公众对 AI 的信任,从而破坏对我们医疗机构的信任。本文有两个贡献。首先,它描述了医疗 AI 中的主要概念、技术和人文挑战。其次,它提出了一个解决方案,该方案取决于专门从事医疗 AI 技术开发、验证和操作的新专家组的教育和认证。这些小组将需要维护我们医疗机构的信任。