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利用人工智能进行矽肺和结核病的大量识别:一种生物伦理方法。

Using Artificial Intelligence for High-Volume Identification of Silicosis and Tuberculosis: A Bio-Ethics Approach.

机构信息

School of Population and Public Health, The University of British Columbia, Vancouver, BC, Canada.

School of Public Health and Family Medicine, University of Cape Town, Cape Town, South Africa.

出版信息

Ann Glob Health. 2021 Jul 1;87(1):58. doi: 10.5334/aogh.3206. eCollection 2021.

Abstract

Although Artificial Intelligence (AI) is being increasingly applied, considerable distrust about introducing "disruptive" technologies persists. Intrinsic and contextual factors influencing where and how such innovations are introduced therefore require careful scrutiny to ensure that health equity is promoted. To illustrate one such critical approach, we describe and appraise an AI application - the development of computer assisted diagnosis (CAD) to support more efficient adjudication of compensation claims from former gold miners with occupational lung disease in Southern Africa. In doing so, we apply a bio-ethical lens that considers the principles of beneficence, non-maleficence, autonomy and justice and add explicability as a core principle. We draw on the AI literature, our research on CAD validation and process efficiency, as well as apprehensions of users and stakeholders. Issues of concern included AI accuracy, biased training of AI systems, data privacy, impact on human skill development, transparency and accountability in AI use, as well as intellectual property ownership. We discuss ways in which each of these potential obstacles to successful use of CAD could be mitigated. We conclude that efforts to overcoming technical challenges in applying AI must be accompanied from the onset by attention to ensuring its ethical use.

摘要

虽然人工智能(AI)的应用越来越广泛,但人们对引入“颠覆性”技术仍存在相当大的不信任。因此,需要仔细审查内在和背景因素,以确保促进健康公平。为了说明这样一种关键方法,我们描述并评估了一种人工智能应用——开发计算机辅助诊断(CAD),以支持更有效地裁决南非南部职业性肺病前金矿工人的赔偿要求。在这样做的过程中,我们应用了一种生物伦理视角,考虑了善行、不伤害、自主和公正的原则,并将可解释性作为一个核心原则。我们借鉴了人工智能文献、我们对 CAD 验证和流程效率的研究,以及用户和利益相关者的担忧。关注的问题包括人工智能的准确性、人工智能系统的有偏见训练、数据隐私、对人类技能发展的影响、人工智能使用的透明度和问责制,以及知识产权所有权。我们讨论了如何减轻这些可能阻碍 CAD 成功使用的潜在障碍。我们得出结论,在努力克服应用人工智能的技术挑战的同时,必须从一开始就注意确保其道德使用。

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