The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Google DeepMind and healthcare in an age of algorithms
Open accessAbstract
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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“For example, when Google DeepMind collaborated with the Royal Free Hospital, they encountered a conflict between protecting the privacy of patient data, and their goal of using AI to improve early diagnosis of acute kidney injury [20].”
From The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions (2019) · cites this paper as [20]
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Cites
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- Personal communication, O’Brien D, Royal Free press office to Hodson, 15 Jun 2016.
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Dean, B., & Kisselburgh, L. G. (2026) · Springer eBooks · doi:10.1007/978-3-032-12201-8_5
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AI and nanotechnology for precision cancer medicine: Opportunities and challenges
Elumalai, M., Sampath, S., Krishnan, Y., Sree, R. M., & Rishitha, B. N. V. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45321-2.00014-x
Governing AI for Pharmacovigilance in Low-Income Countries: Systems Perspective
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Metadata
- Authors
- Powles, J., & Hodson, H.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Powles, Julia
- ORCID
- Powles, Julia
- Venue
- Health and Technology
- Published
- 2017-03-16
- Volume
- 7
- Issue
- 4
- Pages
- 351-367
- ISSN
- 2190-7188 (ISSN-L)
- 2190-7196
- PMC
- PMC5741783
- OpenAlex
- W2596192549
- Cited by
- 511
- References
- 28
- Citations by year
- 2026: 48 · 2025: 67 · 2024: 61 · 2023: 50 · 2022: 55 · 2021: 78 · 2020: 60 · 2019: 43 · 2018: 41 · 2017: 7
- FWCI
- 24.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Health and Technology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- University of Cambridge
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- DeepMind, acute kidney injury, machine learning in healthcare, artificial intelligence in healthcare, data privacy, power imbalance, health data governance
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