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Google DeepMind and healthcare in an age of algorithms

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...

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Detalles Bibliográficos
Autores principales: Powles, Julia, Hodson, Hal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5741783/
https://www.ncbi.nlm.nih.gov/pubmed/29308344
http://dx.doi.org/10.1007/s12553-017-0179-1
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author Powles, Julia
Hodson, Hal
author_facet Powles, Julia
Hodson, Hal
author_sort Powles, Julia
collection PubMed
description 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.
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spelling pubmed-57417832018-01-04 Google DeepMind and healthcare in an age of algorithms Powles, Julia Hodson, Hal Health Technol (Berl) Original Paper 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. Springer Berlin Heidelberg 2017-03-16 2017 /pmc/articles/PMC5741783/ /pubmed/29308344 http://dx.doi.org/10.1007/s12553-017-0179-1 Text en © The Author(s) 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Original Paper
Powles, Julia
Hodson, Hal
Google DeepMind and healthcare in an age of algorithms
title Google DeepMind and healthcare in an age of algorithms
title_full Google DeepMind and healthcare in an age of algorithms
title_fullStr Google DeepMind and healthcare in an age of algorithms
title_full_unstemmed Google DeepMind and healthcare in an age of algorithms
title_short Google DeepMind and healthcare in an age of algorithms
title_sort google deepmind and healthcare in an age of algorithms
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5741783/
https://www.ncbi.nlm.nih.gov/pubmed/29308344
http://dx.doi.org/10.1007/s12553-017-0179-1
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