Google DeepMind and healthcare in an age of algorithms

By Julia E. Powles, H. Hodson. Health technology, 2017

410 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Health, Toxicology and Mutagenesis, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7f5fef86bb1d687da1fa3c37c66e72f45249cde3

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In Artificial Intelligence

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Citations in the corpus

Privacy and artificial intelligence: challenges for protecting health information in a new era (2021, background): “Google, Microsoft, IBM, Apple and other companies are all “preparing, in their own ways, bids on the future of health and on various aspects of the global healthcare industry [25].”
Artificial intelligence to deep learning: machine intelligence approach for drug discovery (2021, methodology): “Recently, Google’s DeepMind (https:// github. com/ deepm ind) has devised an AI-based tool trained on PDB structural data, referred to as AlphaFold, which can predict the 3D structure of proteins from their amino acid sequences [74].”
The ethics of AI in health care: A mapping review. (2020, background): “As we discuss below, the engagement between Alphabet's AI subsidiary DeepMind and a major UK hospital has attracted the attention of data protection regulators, the press, and academics (Information Commissioner, 2018; Powles and Hodson, 2017).”
The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions (2019, background): “…Data Ethics and Innovation); • Researchers across disciplines whose work aims to inform these ways that AI ethics is put into practice: exploring the technical, philosophical, or legal aspects of using algorithms in ethical ways [20] or synthesising and translating such research into practice [27].”
Artificial Intelligence and the Implementation Challenge (2019, background): “As governments and other actors internationally become interested in developing applications of ML, health-related data are increasingly made available to private entities with the capability of producing AI applications that are relevant to peoples’ health [41-43].”
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need? (2018, background): “We see supporting industry ML practitioners as a critical, upstream step towards fairer algorithm-assisted decision making in the public sector, where systems are often built on top of ML products and APIs developed in industry [19, 38, 81, 91].”
A governance model for the application of AI in health care (2019, methodology): “…episodes like Cambridge Ana-lytica using personal data collected by Facebook for political advertising 11 and the Royal Free London NHS Foundation trust sharing patient data for the development of a clinical application without explicit patient consent 12 present concerns about privacy breaches.”
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. (2021, background): “31 Their application, Streams, containing an algorithm for managing patients with acute kidney injuries,had come under fire when it was revealed that the NHS had given the data of 1.6 million patients without their consent to DeepMind servers to train its algo-rithm.”