Evidence map›Paper›PMID 29308344›Full record

ArticleHealth and technology2017

Google DeepMind and healthcare in an age of algorithms.

Julia Powles, Hal Hodson

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Health and technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 119 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
119citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

119 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  15. Artificial General Intelligence and Its Threat to Public Health.Journal of evaluation in clinical practice · 2025
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59 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Julia PowlesFaculty of Law and Computer Laboratory, University of Cambridge, Cambridge, UK.ORCID 0000-0002-3754-5982
Hal HodsonThe Economist Newspaper, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Artificial intelligenceClinical careConsentData protectionMachine learningPowerPrivacyRegulation

Identifiers

PMID29308344
PMCPMC5741783

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.