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人工智能在医学中的未来伦理:理解协作模式。

The Future Ethics of Artificial Intelligence in Medicine: Making Sense of Collaborative Models.

机构信息

Centre for the Study of Professions, Oslo Metropolitan University, Oslo, Norway.

Department of Global Public Health and Primary Care, University of Bergen, Bergen, Norway.

出版信息

Sci Eng Ethics. 2022 Apr 1;28(2):17. doi: 10.1007/s11948-022-00369-2.


DOI:10.1007/s11948-022-00369-2
PMID:35362822
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8975759/
Abstract

This article examines the role of medical doctors, AI designers, and other stakeholders in making applied AI and machine learning ethically acceptable on the general premises of shared decision-making in medicine. Recent policy documents such as the EU strategy on trustworthy AI and the research literature have often suggested that AI could be made ethically acceptable by increased collaboration between developers and other stakeholders. The article articulates and examines four central alternative models of how AI can be designed and applied in patient care, which we call the ordinary evidence model, the ethical design model, the collaborative model, and the public deliberation model. We argue that the collaborative model is the most promising for covering most AI technology, while the public deliberation model is called for when the technology is recognized as fundamentally transforming the conditions for ethical shared decision-making.

摘要

本文探讨了医生、人工智能设计师和其他利益相关者在基于医学中共享决策的一般前提使应用人工智能和机器学习在伦理上可接受方面的作用。最近的政策文件,如欧盟可信人工智能战略和研究文献,经常表明,通过增加开发者和其他利益相关者之间的合作,可以使人工智能在伦理上被接受。本文阐述并考察了在患者护理中设计和应用人工智能的四种核心替代模式,我们称之为普通证据模型、伦理设计模型、协作模型和公共审议模型。我们认为,对于涵盖大多数人工智能技术来说,协作模型是最有前途的,而当技术被认为从根本上改变了伦理共享决策的条件时,则需要采用公共审议模型。

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本文引用的文献

[1]
Trust and medical AI: the challenges we face and the expertise needed to overcome them.

J Am Med Inform Assoc. 2021-3-18

[2]
Identifying Ethical Considerations for Machine Learning Healthcare Applications.

Am J Bioeth. 2020-11

[3]
How to reach trustworthy decisions for caesarean sections on maternal request: a call for beneficial power.

J Med Ethics. 2020-10-14

[4]
How to achieve trustworthy artificial intelligence for health.

Bull World Health Organ. 2020-1-27

[5]
Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare.

Bull World Health Organ. 2020-1-27

[6]
Artificial Intelligence in Clinical Neuroscience: Methodological and Ethical Challenges.

AJOB Neurosci. 2020

[7]
On the ethics of algorithmic decision-making in healthcare.

J Med Ethics. 2020-3

[8]
A governance model for the application of AI in health care.

J Am Med Inform Assoc. 2020-3-1

[9]
Social Impact Under Severe Uncertainty: The Role of Neuroethicists at the Intersection of Neuroscience, AI, Ethics, and Policymaking.

AJOB Neurosci. 2019

[10]
Algorithmic Decision-Making Based on Machine Learning from Big Data: Can Transparency Restore Accountability?

Philos Technol. 2018

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