Cited by 511Artificial Intelligence in Healthcare and EducationEthics and Social Impacts of AIDigitalization, Law, and Regulation

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 access

Powles, Julia, Hodson, Hal·2017·Health and Technology

DOI 10.1007/s12553-017-0179-1PMC PMC5741783

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1007/s12553-017-0179-1

Cites

28 of 28 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Machine learning: Trends, perspectives, and prospects
Jordan, M. I., & Mitchell, T. M. (2015) · Science · doi:10.1126/science.aaa8415 · Cited by 9,955
The Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796
The Black Box Society: The Secret Algorithms That Control Money and Information
(2016) · Contemporary Sociology: A Journal of Reviews · doi:10.1177/0094306116641409c · Cited by 1,334
The social licence for research: why care.data ran into trouble
Carter, P., Laurie, G. T., & Dixon‐Woods, M. (2015) · Journal of Medical Ethics · doi:10.1136/medethics-2014-102374 · Cited by 348
Governing Knowledge Commons
(2014) · doi:10.1093/acprof:oso/9780199972036.001.0001 · Cited by 304
The economic impact of acute kidney injury in England
Kerr, M., Bedford, M., Matthews, B. B., & O'Donoghue, D. J. (2014) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfu016 · Cited by 273
Decision support systems: The next decade
Keen, P. (1987) · Decision Support Systems · doi:10.1016/0167-9236(87)90180-1 · Cited by 271
What is the real impact of acute kidney injury?
Bedford, M., Stevens, P. E., Wheeler, T. W., & Farmer, C. (2014) · BMC Nephrology · doi:10.1186/1471-2369-15-95 · Cited by 93
Acute kidney injury—how does automated detection perform?
Sawhney, S., Fluck, N., Marks, A., Prescott, G. J., Simpson, W. G., Tomlinson, L. A., & Black, C. (2015) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfv094 · Cited by 73
The ethics of big data as a public good: which public? Whose good?
Taylor, L. (2016) · Philosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rsta.2016.0126 · Cited by 70

Show all 28 references

Research works printed but not linked: 6

Personal communication, O’Brien D, Royal Free press office to Hodson, 14 Jul 2016.
Personal communication, O’Brien D, Royal Free press office to Hodson, 15 Jun 2016.
Personal communication, CQC press office to Hodson, 14 Oct 2016.
Data Protection Act 1998 (UK), s.1(1), 4.
For a comprehensive and critical analysis of these concepts, see Van Alsenoy B. Regulating data protection: the allocation of responsibility and risk among actors involved in personal data processing. 2016. KU Leuven doctoral thesis. https://lirias.kuleuven.be/bitstream/123456789/545027/1/PhD_thesis_Van_Alsenoy_Brendan_archived.pdf .
Charette RN. Automated to death. IEEE Spectrum. 15 Dec 2009. http://spectrum.ieee.org/computing/software/automated-to-death .

Sorted by citations

Items 401–500 of 511

Electronic Health Record Research in Critical Care: The End of the Randomized Controlled Trial?
Harris, S. K., MacCallum, N. S., & Brealey, D. A. (2018) · Annual update in intensive care and emergency medicine · doi:10.1007/978-3-319-73670-9_50
Margaret McCartney: Tech companies, healthcare, and a clash of cultures
McCartney, M. (2018) · BMJ · doi:10.1136/bmj.k2143
Clinical translation of optical coherence tomography for the diagnosis of otitis media: validation and tracking of biofilms on the tympanic membrane and development of a novel automated classification and diagnostic platform
Monroy, G. L. (2018) · Illinois Digital Environment for Access to Learning and Scholarship (University of Illinois at Urbana-Champaign)
Could Smart Communities Improve the Efficiency of Cancer Services in Sheffield?
Woolliscroft, T. (2018) · Sheffield Hallam University · doi:10.7190/shu-thesis-00185
Big Data and Healthcare
Paganoni, M. C. (2019) · Springer eBooks · doi:10.1007/978-3-030-16788-2_3
Nadchodzi era sztucznej inteligencji w ochronie wzroku (i nie tylko...)
Pniewski, J. (2018) · ReIn UW (University of Warsaw)
Research Progress of CDT
Yuting, G., Shen, S., & Sanhong, D. (2019) · International Conference on Bioinformatics and Biomedical Science (ICBBS) · doi:10.1145/3369166.3369183
Balancing in the GDPR
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0005
Conditions of the Right to Erasure
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0004
Open Questions on Balancing in the GDPR
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0007
The Right to Erasure in Practice
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0008
Scope of the Right to Erasure
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0003
Gezondheidsgegevens als nieuw zakenmodel
Peleman, R. A., BIESEN, W. V., Decruyenaere, J. M. A., & Sterckx, S. (2021) · Tijdschrift voor Geneeskunde · doi:10.47671/tvg.77.20.125
Big Tech Platforms in Health Research: Re-purposing Big Data Governance in Light of the GDPR’s Research ExemptionPreprint
Marelli, L., Testa, G., & Hoyweghen, I. V. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3712168
Regulating Automated Healthcare and Research Technologies
Brownsword, R. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108620024.033
Private and Public Dimensions of Health Research Regulation
Laurie, G. T. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108620024.028
Tools, Processes and Actors
Dove, E. S., & Sethi, N. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108620024.013
Telemedicine in hematology; long-term implications of a short-term experience during COVID-19 pandemic
Mandal, P. K., & Shekhawat, P. S. (2021) · Journal of Hematology and Allied Sciences · doi:10.25259/jhas_15_2021
Critérios de seleção de patologias para investigação em Saúde
Marques, P. S. (2021) · CADERNOS IBERO-AMERICANOS DE DIREITO SANITÁRIO · doi:10.17566/ciads.v10i3.784
Economic Inequality and the Right to Social Security: Contested Meanings and Potential Roles
Goldblatt, B. (2021) · Cambridge University Press eBooks · doi:10.1017/9781009006545.012
How Can Economists Help Human Rights Practitioners to Measure Changes in Economic Inequalities?
Porter, C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781009006545.005
Growing Inequality and Risks to Social Rights in Our New Data Economy
Williams, C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781009006545.008
The Potential Impact of the Right to Housing to Address Vertical Inequalities
Silva, V. (2021) · Cambridge University Press eBooks · doi:10.1017/9781009006545.015
Education, Income Inequality and the Right to Participate in Cultural Life
Murphy, J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781009006545.013
Data science and its application in anesthesiology
Abbasi, S., Ahmed, U., & Khan, F. A. (2022) · Anaesthesia Pain & Intensive Care · doi:10.35975/apic.v26i1.1757
Introduction
MacNaughton, G., Frey, D. F., & Porter, C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781009006545.001
Bibliography
(2020) · Wiley eBooks · doi:10.1002/9781119644682.biblio
Index
MacNaughton, G., Frey, D. F., & Porter, C. (2021) · Cambridge University Press eBooks · doi:10.1017/9781009006545.016
Features
(2019) · Asia-Pacific Biotech News · doi:10.1142/s0219030319000533
Summary and Conclusion
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0009
DATA FIDUCIARIES AND FAIR PLAY: Overcoming the Personal Data Protection Problem
(2018) · doi:10.5539/9780978430139
Introduction
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0001
Balancing Scenarios
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.003.0006
Artificial Intelligence-based Diagnostic Design for Precision Cancer Nanomedicine
Misra, R., & Sampath, M. (2022) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815050561122010007
THE SCRUTINY OF AI, ML, BIG DATA,DEEP LEARNING AND OTHER TECHNICAL VOWS AND CALLS IN NEPHROLOGY
Sharma, M., & Bajwa, M. S. (2022) · International Conference on Emerging Trends in Engineering and Technology (ICETET) · doi:10.1109/icetet-sip-2254415.2022.9791574
Over 10 years “Health and Technology”
Stoeva, M. S., Low, C. J., & Baumann, C. (2022) · Health and Technology · doi:10.1007/s12553-022-00682-7
Nudging Consent & the New Opt-Out System to the Processing of Health Data in EnglandPreprint
Mészáros, J., Ho, C., & Compagnucci, M. C. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4087791
Gilding Artificial Lilies: Artificial Intelligence’s Legacies of Technological Overstatement, Embellishment, and Hyperbole
Oravec, J. A. (2022) · Social and cultural studies of robots and AI · doi:10.1007/978-3-031-14013-6_6
Place du numérique et de l’intelligence artificielle dans l’activité médicale. Éléments pour une réflexion commune entre médecins et entreprises
Duguet, A. (2022) · Presses de l'Université Toulouse Capitole eBooks · doi:10.4000/books.putc.15470
Intelligent Drug Design and Use for Cancer Treatment: The Roles of AI and Precision Oncology in Targeting Patient-Specific Splicing Profiles
Hull, R., Manabile, M. A., & Dlamini, Z. (2023) · Springer eBooks · doi:10.1007/978-3-031-21506-3_11
Graduate and postgraduate education at a crossroads
Miller, S. M., Moos, W. H., Munk, B. H., Munk, S. A., Hart, C. P., & Spellmeyer, D. C. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-824304-6.00009-2
A systems approach to implementing ethics in a COVID-19 AI application: A qualitative study
Goirand, M., Austin, E. E., & Clay‐Williams, R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15299-3.00011-7
Artificial Intelligence in Healthcare
Singh, K., Srivastava, V. K., & Shukla, A. (2024) · Advances in bioinformatics and biomedical engineering book series · doi:10.4018/979-8-3693-1922-2.ch001
Implementing AI in endoscopy
Abbasi, A. A., & Menon, S. (2024) · Gastrointestinal Nursing · doi:10.12968/gasn.2024.22.sup3.s3
Foreword
Nemitz, P. (2020) · doi:10.1093/oso/9780198847977.002.0005
Note to the Readers
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0012
Table of Cases
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0009
Table of Legislation
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0010
Series Editors’ Preface
Kuner, C., & Greenleaf, G. (2020) · doi:10.1093/oso/9780198847977.002.0006
Copyright Page
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0004
List of Abbreviations
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.002.0011
Impact of Artificial Intelligence (AI) and Internet of Things (IOT) On the Healthcare Sector: A Review
Aich, A., & Banerjee, K. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815223255124010011
Analyse- und Reportingwerkzeuge
Euteneier, A., Eberlein-Gonska, M., & Blum, S. F. U. (2024) · Erfolgskonzepte Praxis- & Krankenhaus-Management · doi:10.1007/978-3-662-67565-6_45
Artificial Intelligence in Community and Hospital Pharmacy
Yadav, S., Jain, P., Vinchurkar, K., & Mane, S. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815305753124010007
Current trends of computational tools and artificial intelligence in geriatric medicine
Awuah, W. A., Yong, B. M. O., Mikhailova, T., Ng, J. C., Abdul‐Rahman, T., Yarlagadda, R., Tedeschi, A. J., Miteu, G. D., Garg, T., David, L., et al. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-15702-8.00023-3
Artificial Intelligence in Kidney Diseases: The Reality and Prospects
Al-Adl, A. S. (2024) · International Journal of Medical Arts · doi:10.21608/ijma.2024.324175.2044
Corporate Governance And AI Ethics In Tech Companies: A Comparative Study Of The US And UKPreprint
Reddy, P. A. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5000653
Importance of Identifying Consent
Gupta, I. (2024) · Springer eBooks · doi:10.1007/978-981-99-6778-0_5
Simplifying cardiology research abstracts: assessing ChatGPT’s readability and comprehensibility for non-medical audiencesPreprint
Malkani, K. V., Falk, Z. E., Zhang, R., Hughes, R., Tawde, P., Parker, M., Collins, G. P., Maizes, D., Zhao, A., & Kini, V. (2025) · medRxiv · doi:10.1101/2025.03.21.25324378
Artificial Intelligence in Pharmaceutical Drug Development-challenges and the Way Forward
Krishnaswami, V., Muruganantham, S., Raja, J., Arthanari, S., Selvaraj, B. R., & Dharmian, J. P. (2025) · Current Artificial Intelligence · doi:10.2174/0129503752359512250325061847
Conflicts and complexities around intellectual property and value sharing of artificial intelligence healthcare solutions in public-private partnerships: A qualitative study
Alami, H., Rivard, L., Lehoux, P., Ahmed, M. A. A., Soubra, R., Rouquet, R., Fleet, R. P., & Fortin, J. (2025) · SSM - Health Systems · doi:10.1016/j.ssmhs.2025.100093
Innovations Advancing AI-driven Technologies to Patient Outcomes in Automotive and other Business AreasPreprint
Ogbatue, C. M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5367149
AI-Driven Knowledge Creation for Predictive Healthcare
Asghar, M., Sabir, A. A., Khan, M. T., Munir, N., Shahzad, M. A., & Ullah, A. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2362-6.ch011
Cloud-Simulated Federated Learning for Medical Image Analysis
Oviya, S., M, R. S., Ananthi, A., & Anandakumar, S. (2025) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds65426.2025.11167719
Exploring AI Impact on the Physician–Patient Relationship: From Confucian and Daoist Views
Fan, R., & Ho, M. (2025) · Springer eBooks · doi:10.1007/978-981-96-7185-4_11
Convergence of machine learning and artificial intelligence in pharmacological implications
Lahkar, R., Kalita, A. J., Kalita, P., Devi, C., & Kalita, P. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-14061-7.00007-5
Monopolizing data, extracting value: Industry 4.0 technologies and intellectual monopoly in healthcare
Predo, R. M., Fonseca, C. V. C., Sarti, F., & Ballini, R. (2025) · Competition & Change · doi:10.1177/10245294251394254
Additional file 1 of Artificial intelligence for good health: a scoping review of the ethics literature
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · Figshare · doi:10.6084/m9.figshare.14037896
Additional file 4 of Artificial intelligence for good health: a scoping review of the ethics literature
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · Figshare · doi:10.6084/m9.figshare.14037905
Additional file 2 of Artificial intelligence for good health: a scoping review of the ethics literature
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · Figshare · doi:10.6084/m9.figshare.14037899
Additional file 3 of Artificial intelligence for good health: a scoping review of the ethics literature
Murphy, K., Ruggiero, E. D., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · Figshare · doi:10.6084/m9.figshare.14037902
Safeguarding Privacy of Sensitive E-health Data Against AI Predictive Algorithm Threats
Tahenni, A., & Belkhir, A. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6432-0_21
(In)adequate Data Protection and Algorithmic Predictive Analysis: An Unresolved Battle
Horzyk, A. M. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11228804
Strategic Analysis of DeepMind Technologies Limited: An Exploratory Case Study of AI Innovation, Ethics, and Business Evolution
Aithal, P. S. (2025) · Poornaprajna International Journal of Teaching & Research Case Studies (PIJTRCS) · doi:10.64818/pijtrcs.3107.8494.0020
Governance and Management of Medical Artificial Intelligence
Guan, J. (2025) · Springer eBooks · doi:10.1007/978-981-95-2806-6_14 · Cited by 2
Ethical Considerations in AI-Driven Drug Development and Pharmaceutical Industry
Bari, Y. C., Wani, M. S., Nagare, G. D., Chogale, M. M., & Pai, R. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-5258-9.ch010
Case Studies and Success Stories
Majumdar, S., & Misra, B. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-14557-4_7
38121 Data-driven Approaches in Biomedical Research
Milne, R. (2025) · De Gruyter eBooks · doi:10.1515/9783111247854-021
Machine Learning Approaches for Precision Risk Assessment of Transfusion Adverse Reactions: A Scikit-learn-Based Review
Wang, Y., Zhou, W., & Huang, Y. (2026) · Journal of Visualized Experiments · doi:10.3791/69658
The Intersection of AI and genomics in health and disease: Advancements and applications
Kaushik, L., Vivek, A., Arora, S., Hamid, F., Mukherjee, K., Bisht, N., Chaudhary, S., Shukla, J., Nawani, S., & Kumar, S. (2026) · Progress in molecular biology and translational science · doi:10.1016/bs.pmbts.2026.01.013
Guiding the Development of AI in Healthcare Through Ethical Considerations and Effective Governance
Jain, R., & Chaudhary, G. (2026) · Studies in computational intelligence · doi:10.1007/978-3-032-03985-9_10
The efficacy and use of Artificial Intelligence in medical data management and decision-making to future horizons
Tuncȩl, N., & Bilgiç, E. (2025) · International Journal of Radiology & Radiation Therapy · doi:10.15406/ijrrt.2025.12.00418
Ethics Applied to Digital Healthcare: Navigating the Moral Landscape
Niazkhani, Z., Wallenburg, I., Hendriks, J. M., & Wehrens, R. (2026) · Springer eBooks · doi:10.1007/978-3-031-89820-4_24
A detailed review of large language models in the healthcare sector
Ozsahin, D. U., Emegano, D. I., Uzun, B., & Özsahin, İ. (2026) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-026-00327-z
Algorithmic care and consent: reframing ethical data mining in health-care systems
Rahman, H. (2026) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-07-2025-0190
Clinical Informatics and Artificial Intelligence in Healthcare
Greenwood, J. D., & Qian, R. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-2577-4.ch004
Strategic PR as an Imperative of Internationalization: Mechanisms of Legitimation and Capital Attraction for High-Tech Companies in the AI, IT, and Biotechnology Sectors
Krasnoslobodtseva, L. (2026) · Universal Library of business and economics. · doi:10.70315/uloap.ulbec.2026.0301014
Design of multi-FPGAs platform in the cloud for neural network applications
Chen, W. (2021) · doi:10.70675/ce233682z21c8z4566zb3c2ze14c05722e87
A Combination of Human Wisdom and Artificial Intelligence will Win the Game
He, L., Harold, S. F. (., & Frydrysiak-Brzozowska, A. (2026) · Newborn · doi:10.5005/newborn-5-1-iv
Data-driven decision-making in healthcare: Unlocking value through web analytics
Kamaşak, R., Yalcinkaya, B., & Gorener, R. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-36554-6.00020-9
Artificial Intelligence and Natural Intelligence in Physical Medicine and Rehabilitation: A Synergistic Framework for Future Practice
Badhal, S., Vashist, A., & Singh, R. (2026) · Indian Journal of Physical Medicine and Rehabilitation · doi:10.4103/ijpmr.ijpmr_83_25 · Cited by 1
Stances toward data governance: Negotiating tensions in data sharing for artificial intelligence in healthcare
Gubser, R., Schulte‐Althoff, M., Pohle, J., Heinemann, N., Balzer, F., & Fürstenau, D. (2026) · Information and Organization · doi:10.1016/j.infoandorg.2026.100625
Ethical and Legal Challenges in AI-Based Diagnosis and Treatment
Mishra, S., & Gupta, P. (2026) · Cognitive systems monographs · doi:10.1007/978-3-032-18782-6_9
Human-Centered AI in Healthcare
Majumder, N. N., & Adebiyi, B. O. (2026) · Springer eBooks · doi:10.1007/978-981-95-3658-0_112
Comparative Study of Arbitration Regulation in Artificial Intelligence (AI) Technology Disputes from the Perspective of Indonesian and European Union Law
Sin, L. G., Shanty, W. Y., & Ariyanti, A. (2025) · Society · doi:10.33019/society.v13i3.971
The ethics of regulation: Social contract insights on the 2024 European Union Artificial Intelligence Act
Loriga, L. (2026) · Ethics & bioethics · doi:10.2478/ebce-2026-0014
784785Chapter 31 AI and RL Meets Healthcare: Ethical, Regulatory, and Implementation Challenges in Clinical Environments
Ashraf, G. Y., Shrivastava, R., & Sivarethinamohan, R. (2026) · De Gruyter eBooks · doi:10.1515/9783111633909-031
Stress Evaluation through Electrophysiological Parameters: Current Research Perspectives, Legal and Ethical Aspects and Future Direc-tions
Periferakis, A., Troumpata, L., Periferakis, A.-T., Periferakis, K., Timofticiuc, I.-A., Georgatos-Garcia, S., Touriki, G., Bozatzi, A., Săvulescu-Fiedler, I., & Iftime, A. (2026) · Balneo and PRM Research Journal · doi:10.12680/balneo.2026.1019
Ethics and AI in Healthcare: Balancing Innovation with Patient Rights and Organizational Culture
Vashistha, R., Bhushan, P., & Jewandah, S. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898816452126010012
A tale of two controversies: situated AI frictions and the generative AI tsunami
Poletti, C., Marres, N., Ricci, D., Katzenbach, C., Henkes, L. M., & Dandurand, G. (2026) · AI & Society · doi:10.1007/s00146-026-03141-3

Authors: Powles, J., & Hodson, H.
Affiliations: University of Cambridge (GB)
Corresponding author: Powles, Julia
ORCID: Powles, Julia
Venue: Health and Technology
Publisher: Springer Science and Business Media LLC
Published: 2017-03-16
Volume: 7
Issue: 4
Pages: 351-367
ISSN: 2190-7188 (ISSN-L)
2190-7196
DOI: 10.1007/s12553-017-0179-1
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (unspecified)
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Google DeepMind and healthcare in an age of algorithms

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks