Powles Julia, Hodson Hal
Faculty of Law and Computer Laboratory, University of Cambridge, Cambridge, UK.
The Economist Newspaper, London, UK.
Health Technol (Berl). 2017;7(4):351-367. doi: 10.1007/s12553-017-0179-1. Epub 2017 Mar 16.
Data-driven tools and techniques, particularly machine learning methods that underpin artificial intelligence, offer promise in improving healthcare systems and services. One of the companies aspiring to pioneer these advances is DeepMind Technologies Limited, a wholly-owned subsidiary of the Google conglomerate, Alphabet Inc. In 2016, DeepMind announced its first major health project: a collaboration with the Royal Free London NHS Foundation Trust, to assist in the management of acute kidney injury. Initially received with great enthusiasm, the collaboration has suffered from a lack of clarity and openness, with issues of privacy and power emerging as potent challenges as the project has unfolded. Taking the DeepMind-Royal Free case study as its pivot, this article draws a number of lessons on the transfer of population-derived datasets to large private prospectors, identifying critical questions for policy-makers, industry and individuals as healthcare moves into an algorithmic age.
数据驱动的工具和技术,特别是作为人工智能基础的机器学习方法,有望改善医疗系统和服务。渴望引领这些进步的公司之一是DeepMind Technologies Limited,它是谷歌集团Alphabet Inc.的全资子公司。2016年,DeepMind宣布了其首个重大健康项目:与伦敦皇家自由国民保健服务基金会信托基金合作,以协助管理急性肾损伤。该合作最初受到极大热情的欢迎,但随着项目的推进,由于缺乏透明度和开放性,隐私和权力问题成为了严峻挑战。本文以DeepMind与皇家自由医院的案例研究为核心,就将人口衍生数据集转移给大型私人企业的情况吸取了一些教训,为医疗保健进入算法时代的政策制定者、行业和个人提出了关键问题。