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公平份额:通过互利的结构和发展伙伴关系建设和受益于医疗保健人工智能。

Fair shares: building and benefiting from healthcare AI with mutually beneficial structures and development partnerships.

机构信息

Department of Radiology, Gloucestershire Hospitals NHS Foundation Trust, Gloucestershire, UK.

Department of Radiology, The Royal Marsden Hospital NHS Foundation Trust, London, UK.

出版信息

Br J Cancer. 2021 Oct;125(9):1181-1184. doi: 10.1038/s41416-021-01454-2. Epub 2021 Jul 14.

Abstract

Artificial intelligence (AI) algorithms are used in an increasing range of aspects of our lives. In particular, medical applications of AI are being developed and deployed, including many in image analysis. Deep learning methods, which have recently proved successful in image classification, rely on large volumes of clinical data generated by healthcare institutions. Such data is collected from their served populations. In this opinion article, using digital mammographic screening as an example, we briefly consider the background to AI development and some issues around its deployment. We highlight the importance of high quality clinical data as fundamental to these technologies, and question how the ownership of resultant tools should be defined. Though many of the ethical issues concerning the development and use of medical AI technologies continue to be discussed, the value of the data on which they rely remains a subject that is seldom considered. This potentially controversial issue can and should be addressed in a way which is beneficial to all parties, particularly the population in general and the patients we serve.

摘要

人工智能(AI)算法正在我们生活的方方面面得到越来越广泛的应用。特别是,人工智能在医学领域的应用正在开发和部署,包括许多在图像分析方面的应用。深度学习方法在图像分类方面最近取得了成功,这些方法依赖于医疗机构生成的大量临床数据。这些数据是从其服务的人群中收集的。在这篇观点文章中,我们以数字乳腺 X 线筛查为例,简要讨论了 AI 发展的背景以及其部署方面的一些问题。我们强调了高质量临床数据对这些技术的重要性,并质疑如何定义由此产生的工具的所有权。尽管有关医疗人工智能技术的开发和使用的许多伦理问题仍在讨论中,但它们所依赖的数据的价值仍然是一个很少被考虑的问题。这个潜在的争议问题可以而且应该以一种对所有各方都有利的方式来解决,特别是对一般人群和我们服务的患者。

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