Google DeepMind and healthcare in an age of algorithms

Powles, Julia; Hodson, Hal

doi:10.1007/s12553-017-0179-1

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

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?

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.
“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]

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 · see citing works
The Black Box Society: The Secret Algorithms That Control Money and Information
Pasquale, F. (2015) · doi:10.4159/harvard.9780674736061 · Cited by 3,796 · see citing works
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 · see citing works
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 · see citing works
Governing Knowledge Commons
(2014) · doi:10.1093/acprof:oso/9780199972036.001.0001 · Cited by 304 · see citing works
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 · see citing works
Decision support systems: The next decade
Keen, P. G. W. (1987) · Decision Support Systems · doi:10.1016/0167-9236(87)90180-1 · Cited by 266 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Automated analysis of retinal imaging using machine learning techniques for computer vision
Fauw, J. D., Keane, P. A., Tomašev, N., Visentin, D., van den Driessche, G., Johnson, M., Hughes, C., Chu, C., Ledsam, J. R. E., Back, T., et al. (2016) · F1000Research · doi:10.12688/f1000research.8996.1 · Cited by 48 · see citing works
The Case That Won't Be Forgotten
Powles, J. (2015) · eYLS (Yale Law School) · Cited by 40 · see citing works
Self-citation
Acute kidney injury risk assessment at the hospital front door: what is the best measure of risk?
Roberts, G. W., Phillips, D., McCarthy, R., Bolusani, H., Mizen, P., Hassan, M., Hooper, R., Saddler, K., Hu, M., Lodhi, S., et al. (2015) · Clinical Kidney Journal · doi:10.1093/ckj/sfv080 · Cited by 32 · see citing works
The Caldicott Report on the review of patient-identifiable information – executive summary December 1997
Eckstein, S. (2003) · Cambridge University Press eBooks · doi:10.1017/cbo9780511550089.056 · Cited by 31 · see citing works
Robotics and Artificial Intelligence
Deitel, H. M., & DEITEL, B. (1985) · Elsevier eBooks · doi:10.1016/b978-0-12-209010-3.50025-6 · Cited by 25 · see citing works
Father and son prosecuted for breach of data protection act
(1998) · Computer Fraud & Security · doi:10.1016/s1361-3723(98)90167-1 · Cited by 22 · see citing works
Acute kidney injury
Connell, A., & Laing, C. M. (2015) · Clinical Medicine · doi:10.7861/clinmedicine.15-6-581 · Cited by 20 · see citing works
Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans
Chu, C., Fauw, J. D., Tomašev, N., Romera‐Paredes, B., Hughes, C., Ledsam, J. R. E., Back, T., Montgomery, H., Rees, G. E., Raine, R., et al. (2016) · F1000Research · doi:10.12688/f1000research.9525.1 · Cited by 19 · see citing works
Too Much of a Good Thing? A Governing Knowledge Commons Review of Abundance in Context
Madison, M. J., Frischmann, B. M., Sanfilippo, M. R., & Strandburg, K. J. (2022) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2022.959505 · Cited by 17 · see citing works
NHS data sharing deal with Google prompts concern
Hawkes, N. (2016) · BMJ · doi:10.1136/bmj.i2573 · Cited by 13 · see citing works
Can decision support systems work for acute kidney injury?: FIGURE 1:
Kellum, J. A., Kane‐Gill, S. L., & Handler, S. M. (2015) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfv285 · Cited by 9 · see citing works
The Ethics of Big Data as a Public Good: Which Public? Whose Good?Preprint
Taylor, L. (2016) · SSRN Electronic Journal · doi:10.2139/ssrn.2820580 · Cited by 5 · see citing works
The acute kidney injury e-alert and clinical care bundles: the road to success is always under construction
Meijers, B. K. I., Moor, B. D., & den Bosch, B. V. (2016) · Nephrology Dialysis Transplantation · doi:10.1093/ndt/gfw213 · Cited by 4 · see citing works
Google’s Larry Page wants to save 100,000 lives but big data isn’t a cure all
Boiten, E. (2014) · doi:10.64628/ab.ndug4nkhv · Cited by 2 · see citing works
Google's NHS data deal
(2016) · The New Scientist · doi:10.1016/s0262-4079(16)30919-8 · Cited by 1 · see citing works
Peer Review Report For: Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; peer review: 2 approved]
Yang, Y. (2016) · F1000Research · doi:10.5256/f1000research.9679.r15056 · Cited by 1 · see citing works
Peer Review Report For: Automated analysis of retinal imaging using machine learning techniques for computer vision [version 1; peer review: 2 approved]
Sandrine, Z. (2016) · F1000Research · doi:10.5256/f1000research.9679.r14781 · Cited by 1 · see citing works
Peer Review Report For: Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans [version 1; peer review: 1 approved with reservations]
Feng, Y. (2016) · F1000Research · doi:10.5256/f1000research.10262.r17312 · Cited by 1 · see citing works

Flagged first, then by citations

Research works printed but not linked: 6

[31]Personal communication, O’Brien D, Royal Free press office to Hodson, 14 Jul 2016.
[53]Personal communication, O’Brien D, Royal Free press office to Hodson, 15 Jun 2016.
[56]Personal communication, CQC press office to Hodson, 14 Oct 2016.
[63]Data Protection Act 1998 (UK), s.1(1), 4.
[67]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.
[92]Charette RN. Automated to death. IEEE Spectrum. 15 Dec 2009. http://spectrum.ieee.org/computing/software/automated-to-death.

Sorted by citations

Artificial intelligence to deep learning: machine intelligence approach for drug discovery
Gupta, R., Srivastava, D., Sahu, M., Tiwari, S., Ambasta, R. K., & Kumar, P. (2021) · Molecular Diversity · doi:10.1007/s11030-021-10217-3 · Cited by 1,473 · see citing works
“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].”
Method
The ethics of AI in health care: A mapping review
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113172 · Cited by 1,105 · see citing works
Artificial intelligence in drug development: Present status and future prospects
Mak, K.-K., & Pichika, M. R. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2018.11.014 · Cited by 1,018 · see citing works
Privacy and artificial intelligence: challenges for protecting health information in a new era
Murdoch, B. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00687-3 · Cited by 991 · see citing works
A governance model for the application of AI in health care
Reddy, S., Allan, S., Coghlan, S., & Cooper, P. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz192 · Cited by 979 · see citing works
Improving Fairness in Machine Learning Systems: What Do Industry Practitioners Need?
Holstein, K., Wortman Vaughan, J., Daumé, H., Dudik, M., & Wallach, H. (2019) · Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3290605.3300830 · Cited by 777 · see citing works
AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications
Rashid, A. B., & Kausik, M. A. K. (2024) · Hybrid Advances · doi:10.1016/j.hybadv.2024.100277 · Cited by 764 · see citing works
ChatGPT: the future of discharge summaries?
Patel, S. B., & Lam, K. (2023) · The Lancet Digital Health · doi:10.1016/s2589-7500(23)00021-3 · Cited by 728 · see citing works
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021) · British Medical Bulletin · doi:10.1093/bmb/ldab016 · Cited by 511 · see citing works
Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States
Pesapane, F., Volonté, C., Codari, M., & Sardanelli, F. (2018) · Insights into Imaging · doi:10.1007/s13244-018-0645-y · Cited by 479 · see citing works
Artificial intelligence for good health: a scoping review of the ethics literature.
Murphy, K., Di Ruggiero, E., Upshur, R., Willison, D. J., Malhotra, N., Cai, J. C., Malhotra, N., Lui, V., & Gibson, J. (2021) · BMC Medical Ethics · doi:10.1186/s12910-021-00577-8 · Cited by 435 · see citing works
A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective
Habibzadeh, H., Dinesh, K., Shishvan, O. R., Boggio-Dandry, A., Gaurav Sharma, A., & Soyata, T. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2946359 · Cited by 419 · see citing works
A short guide for medical professionals in the era of artificial intelligence
Meskó, B., & Görög, M. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00333-z · Cited by 409 · see citing works
The Role and Limits of Principles in AI Ethics: Towards a Focus on Tensions
Whittlestone, J., Nyrup, R., Alexandrova, A., & Cave, S. (2019) · Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3306618.3314289 · Cited by 399 · see citing works
Artificial intelligence and the implementation challenge
Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13659 · Cited by 389 · see citing works
SHIFTing artificial intelligence to be responsible in healthcare: a systematic review
Siala, H., & Wang, Y. (2022) · Social Science & Medicine · doi:10.1016/j.socscimed.2022.114782 · Cited by 358 · see citing works
Artificial intelligence: Building blocks and an innovation typology
Paschen, U., Pitt, C., & Kietzmann, J. (2019) · Business Horizons · doi:10.1016/j.bushor.2019.10.004 · Cited by 351 · see citing works
AI in drug discovery and its clinical relevance
Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319 · see citing works
Trust and medical AI: the challenges we face and the expertise needed to overcome them
Quinn, T. P., Senadeera, M., Jacobs, S., Coghlan, S., & Le, V. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa268 · Cited by 287 · see citing works
Ethical, legal, and social considerations of AI-based medical decision-support tools: a scoping review
Čartolovni, A., Tomičić, A., & Lazić Mosler, E. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104738 · Cited by 267 · see citing works
Blockchain technology in healthcare: A systematic review
Saeed, H., Malik, H., Bashir, U., Ahmad, A., Riaz, S., Ilyas, M., Bukhari, W. A., & Khan, M. I. A. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266462 · Cited by 276 · see citing works
Artificial intelligence and augmented intelligence collaboration: regaining trust and confidence in the financial sector
Lui, A. T., & Lamb, G. W. (2018) · Information & Communications Technology Law · doi:10.1080/13600834.2018.1488659 · Cited by 228 · see citing works
Deep learning for wireless capsule endoscopy: a systematic review and meta-analysis
Soffer, S., Klang, E., Shimon, O., Nachmias, N., Eliakim, R., Ben‐Horin, S., Kopylov, U., & Barash, Y. (2020) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2020.04.039 · Cited by 210 · see citing works
Artificial Intelligence, Machine Learning, and Cardiovascular Disease
Mathur, P., Srivastava, S., Xu, X., & Mehta, J. L. (2020) · Clinical Medicine Insights Cardiology · doi:10.1177/1179546820927404 · Cited by 223 · see citing works
Benefits and Risks of AI in Health Care: Narrative Review
Chustecki, M. (2024) · Interactive Journal of Medical Research · doi:10.2196/53616 · Cited by 208 · see citing works
“Recent incidents, such as the misuse of Facebook personal data by Cambridge Analytica and the sharing of patient data without explicit consent by the Royal Free London NHS Foundation Trust, have raised concerns about privacy violations [49,50].”
Canadian Association of Radiologists White Paper on Ethical and Legal Issues Related to Artificial Intelligence in Radiology
Jaremko, J. L., Azar, M., Bromwich, R. J., Lum, A., Cheong, L. H. A., Gibert, M., Laviolette, F., Gray, B., Reinhold, C., Cicero, M. D., et al. (2019) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2019.03.001 · Cited by 200 · see citing works
Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges: Primary Health Care Informatics Working Group Contribution to the Yearbook of Medical Informatics 2019
Liyanage, H., Liaw, S.-T., Jonnagaddala, J., Schreiber, R., Kuziemsky, C., Terry, A. L., & de Lusignan, S. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677901 · Cited by 200 · see citing works
“Deep learning has demonstrated significant results in various non-health and health-related applications using computer vision and natural language processing [5, 6].”
Background
When digital health meets digital capitalism, how many common goods are at stake?
Sharon, T. (2018) · Big Data & Society · doi:10.1177/2053951718819032 · Cited by 188 · see citing works
Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks
Vesnic-Alujevic, L., Nascimento, S., & Pólvora, A. (2020) · Telecommunications Policy · doi:10.1016/j.telpol.2020.101961 · Cited by 180 · see citing works
Machine learning in oral squamous cell carcinoma: Current status, clinical concerns and prospects for future—A systematic review
Alabi, R. O., Youssef, O., Pirinen, M., Elmusrati, M. S., Mäkitie, A. A., Leivo, I. V. V., & Almangush, A. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102060 · Cited by 178 · see citing works
Multi‐Mode/Signal Biosensors: Electrochemical Integrated Sensing Techniques
Han, Q., Wang, H., & Wang, H. (2024) · Advanced Functional Materials · doi:10.1002/adfm.202403122 · Cited by 174 · see citing works
AI, big data, and the future of consent
Andreotta, A. J., Kirkham, N., & Rizzi, M. (2021) · AI & Society · doi:10.1007/s00146-021-01262-5 · Cited by 188 · see citing works
Artificial intelligence in the field of pharmacy practice: A literature review
Chalasani, S. H., Syed, J. M., Ramesh, M., Patil, V., & Kumar, T. M. P. (2023) · Exploratory Research in Clinical and Social Pharmacy · doi:10.1016/j.rcsop.2023.100346 · Cited by 209 · see citing works
Applications of Blockchain Within Healthcare
Bell, L., Buchanan, W. J., Cameron, J., & Lo, O. (2018) · Blockchain in Healthcare Today · doi:10.30953/bhty.v1.8 · Cited by 164 · see citing works
Health Information Management: Implications of Artificial Intelligence on Healthcare Data and Information Management
Stanfill, M. H., & Marc, D. T. (2019) · Yearbook of Medical Informatics · doi:10.1055/s-0039-1677913 · Cited by 170 · see citing works
Performance and exploration of ChatGPT in medical examination, records and education in Chinese: Pave the way for medical AI
Wang, H., Wu, W., Dou, Z., He, L., & Yang, L. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105173 · Cited by 156 · see citing works
Perspective of artificial intelligence in healthcare data management: A journey towards precision medicine
Gupta, N. S., & Kumar, P. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2023.107051 · Cited by 155 · see citing works
Evaluating artificial intelligence in medicine: phases of clinical research
Park, Y., Jackson, G. P., Foreman, M. A., Gruen, D., Hu, J., & Das, A. K. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa033 · Cited by 150 · see citing works
The Era of Radiogenomics in Precision Medicine: An Emerging Approach to Support Diagnosis, Treatment Decisions, and Prognostication in Oncology
Shui, L., Ren, H., Yang, X., Li, J., Chen, Z., Cheng, Y., Zhu, H., & Shui, P. (2021) · Frontiers in Oncology · doi:10.3389/fonc.2020.570465 · Cited by 141 · see citing works
Fit for purpose? The GDPR and the governance of European digital health
Marelli, L., Lievevrouw, E., & Van Hoyweghen, I. (2020) · Policy Studies · doi:10.1080/01442872.2020.1724929 · Cited by 137 · see citing works
Big data governance of personal health information and challenges to contextual integrity
Winter, J. S., & Davidson, E. (2018) · The Information Society · doi:10.1080/01972243.2018.1542648 · Cited by 132 · see citing works
Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence
Chen, Y., Hsiao, T., Lin, C., & Fann, Y. C. (2025) · Journal of Biomedical Science · doi:10.1186/s12929-024-01110-w · Cited by 137 · see citing works
Biomedical Big Data: New Models of Control Over Access, Use and Governance
Vayena, E., & Blasimme, A. (2017) · Journal of Bioethical Inquiry · doi:10.1007/s11673-017-9809-6 · Cited by 120 · see citing works
Why Is the Electronic Health Record So Challenging for Research and Clinical Care?
Holmes, J. H., Beinlich, J., Boland, M. R., Bowles, K. H., Chen, Y., Cook, T. S., Demiris, G., Draugelis, M. E., Fluharty, L., Gabriel, P. E., et al. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1731784 · Cited by 125 · see citing works
Implementing Ethics in Healthcare AI-Based Applications: A Scoping Review
Goirand, M., Austin, E., & Clay-Williams, R. (2021) · Science and Engineering Ethics · doi:10.1007/s11948-021-00336-3 · Cited by 101 · see citing works
Machine learning in medicine: what clinicians should know
Sim, J. Z. T., Fong, Q. W., Huang, W., & Tan, C. H. (2021) · Singapore Medical Journal · doi:10.11622/smedj.2021054 · Cited by 99 · see citing works
Before and beyond trust: reliance in medical AI
Kerasidou, C., Kerasidou, A., Büscher, M., & Wilkinson, S. (2021) · Journal of Medical Ethics · doi:10.1136/medethics-2020-107095 · Cited by 95 · see citing works
Digital Technologies and Data Science as Health Enablers: An Outline of Appealing Promises and Compelling Ethical, Legal, and Social Challenges
Cordeiro, J. V. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.647897 · Cited by 92 · see citing works
Artificial Intelligence: power for civilisation – and for better healthcare
Horgan, D., Romao, M., Morré, S. A., & Kalra, D. (2019) · Public Health Genomics · doi:10.1159/000504785 · Cited by 86 · see citing works
Blockchain Applications in Health Care and Public Health: Increased Transparency
Velmovitsky, P. E., Bublitz, F. M., Fadrique, L. X., & Morita, P. P. (2021) · JMIR Medical Informatics · doi:10.2196/20713 · Cited by 89 · see citing works
A Delphi consensus statement for digital surgery
Lam, K., Abràmoff, M. D., Balibrea, J. M., Bishop, S. M., Brady, R. R. W., Callcut, R. A., Chand, M., Collins, J. W., Diener, M. K., Eisenmann, M., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00641-6 · Cited by 87 · see citing works
Artificial intelligence (AI) in healthcare and biomedical research: Why a strong computational/AI bioethics framework is required?
Bali, J., Garg, R., & Bali, R. T. (2018) · Indian Journal of Ophthalmology · doi:10.4103/ijo.ijo_1292_18 · Cited by 88 · see citing works
Personalizing solidarity? The role of self-tracking in health insurance pricing
McFall, L. (2019) · Economy and Society · doi:10.1080/03085147.2019.1570707 · Cited by 86 · see citing works
Platform power in AI: The evolution of cloud infrastructures in the political economy of artificial intelligence
Luitse, D. M. R. (2024) · Internet Policy Review · doi:10.14763/2024.2.1768 · Cited by 91 · see citing works
Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends
Kumar, K., Kumar, P., Deb, D., Ungureșan, M.-L., & Mureşan, V. (2023) · Healthcare · doi:10.3390/healthcare11020207 · Cited by 85 · see citing works
Observational health research in Europe: understanding the General Data Protection Regulation and underlying debate
van Veen, E.-B. (2018) · European Journal of Cancer · doi:10.1016/j.ejca.2018.09.032 · Cited by 84 · see citing works
QNMF: A quantum neural network based multimodal fusion system for intelligent diagnosis
Qu, Z., Li, Y., & Tiwari, P. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101913 · Cited by 81 · see citing works
Improving fairness in machine learning systems: What do industry practitioners need?Preprint
Holstein, K., Vaughan, J. W., Daumé, H., Dudík, M., & Wallach, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.05239 · Cited by 81 · see citing works
The right to refuse diagnostics and treatment planning by artificial intelligence
Ploug, T., & Holm, S. (2019) · Medicine Health Care and Philosophy · doi:10.1007/s11019-019-09912-8 · Cited by 79 · see citing works
PatientDataChain: A Blockchain-Based Approach to Integrate Personal Health Records
Cernian, A. S., Țigănoaia, B., Sacală, I. Ş., Pavel, A., & Iftemi, A. (2020) · Sensors · doi:10.3390/s20226538 · Cited by 78 · see citing works
Artificial Intelligence and Machine Learning Algorithms
Tyagi, A. K., & Chahal, P. (2019) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-7998-0182-5.ch008 · Cited by 76 · see citing works
Artificial Intelligence in Emergency Medicine: Surmountable Barriers With Revolutionary Potential
Grant, K. L., McParland, A., Mehta, S., & Ackery, A. (2020) · Annals of Emergency Medicine · doi:10.1016/j.annemergmed.2019.12.024 · Cited by 76 · see citing works
Role of Artificial Intelligence in Kidney Disease
Yuan, Q., Zhang, H., Deng, T., Tang, S., Yuan, X., Tang, W., Xie, Y., Ge, H., Wang, X., Zhou, Q., et al. (2020) · International Journal of Medical Sciences · doi:10.7150/ijms.42078 · Cited by 81 · see citing works
Regulating Big Tech expansionism? Sphere transgressions and the limits of Europe’s digital regulatory strategy
Sharon, T., & Gellert, R. M. (2023) · Information Communication & Society · doi:10.1080/1369118x.2023.2246526 · Cited by 76 · see citing works
Big Data in Drug Discovery
Brown, N., Cambruzzi, J., Cox, P. J., Davies, M., Dunbar, J., Plumbley, D., Sellwood, M. A., Sim, A., Williams-Jones, B. I., Zwierzyna, M., et al. (2018) · Progress in medicinal chemistry · doi:10.1016/bs.pmch.2017.12.003 · Cited by 71 · see citing works
The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review
Morley, J., Machado, C., Burr, C., Cowls, J., Taddeo, M., & Floridi, L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3486518 · Cited by 66 · see citing works
A review on the recent applications of deep learning in predictive drug toxicological studies
Sinha, K., Ghosh, N., & Sil, P. C. (2023) · Chemical Research in Toxicology · doi:10.1021/acs.chemrestox.2c00375 · Cited by 64 · see citing works
Exploring healthcare professionals’ perceptions of artificial intelligence: Validating a questionnaire using the e-Delphi method
Shinners, L., Aggar, C., Grace, S., & Smith, S. (2021) · Digital Health · doi:10.1177/20552076211003433 · Cited by 67 · see citing works
How artificial intelligence could transform emergency care
Kachman, M. M., Brennan, I., Oskvarek, J. J., Waseem, T. C., & Pines, J. M. (2024) · The American Journal of Emergency Medicine · doi:10.1016/j.ajem.2024.04.024 · Cited by 62 · see citing works
P5 eHealth: An Agenda for the Health Technologies of the Future
Pravettoni, G., & Triberti, S. (2019) · Springer eBooks · doi:10.1007/978-3-030-27994-3 · Cited by 62 · see citing works
Fold-stratified cross-validation for unbiased and privacy-preserving federated learning
Bey, R., Goussault, R., Grolleau, F., Benchoufi, M., & Porcher, R. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa096 · Cited by 60 · see citing works
COVID-19 contact tracing apps: UK public perceptions
Samuel, G. N., Roberts, S. L., Fiske, A., Lucivero, F., McLennan, S., Phillips, A., Hayes, S., & Johnson, S. B. (2021) · Critical Public Health · doi:10.1080/09581596.2021.1909707 · Cited by 60 · see citing works
Proteogenomics: From next-generation sequencing (NGS) and mass spectrometry-based proteomics to precision medicine
Ang, M. Y., Low, T. Y., Lee, P. Y., Nazarie, W. F. W. M., Guryev, V., & Jamal, R. (2019) · Clinica Chimica Acta · doi:10.1016/j.cca.2019.08.010 · Cited by 60 · see citing works
Wearable Sensors, Data Processing, and Artificial Intelligence in Pregnancy Monitoring: A Review
Liu, L., Pu, Y., Fan, J., Yan, Y., Liu, W., Luo, K., Wang, Y., Zhao, G., Chen, T. P., Poenar, D. P., et al. (2024) · Sensors · doi:10.3390/s24196426 · Cited by 67 · see citing works
What Is Trust? Ethics and Risk Governance in Precision Medicine and Predictive Analytics
Adjekum, A., Ienca, M., & Vayena, E. (2017) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2017.0156 · Cited by 57 · see citing works
Critical Care Health Informatics Collaborative (CCHIC): Data, tools and methods for reproducible research: A multi-centre UK intensive care database
Harris, S. K., Shi, S., Brealey, D. A., MacCallum, N. S., Denaxas, S., Pérez–Suárez, D., Ercole, A., Watkinson, P., Jones, A., Ashworth, S., et al. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.01.006 · Cited by 56 · see citing works
Artificial intelligence in neurosciences: A clinician's perspective
Ganapathy, K., Syed-Abdul, S., & Nursetyo, A. A. (2018) · Neurology India · doi:10.4103/0028-3886.236971 · Cited by 56 · see citing works
Privacy-Preserving Deep Learning With Learnable Image Encryption on Medical Images
Huang, Q.-X., Yap, W. L., Chiu, M.-Y., & Sun, H. (2022) · IEEE Access · doi:10.1109/access.2022.3185206 · Cited by 54 · see citing works
Plastic Surgery and Artificial Intelligence: How ChatGPT Improved Operation Note Accuracy, Time, and Education
Abdelhady, A. M., & Davis, C. R. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.06.002 · Cited by 55 · see citing works
Image Analysis in Histopathology and Cytopathology: From Early Days to Current Perspectives
Mezei, T., Kolcsár, M., Joó, A., & Gurzu, S. (2024) · Journal of Imaging · doi:10.3390/jimaging10100252 · Cited by 64 · see citing works
The Right to Erasure in EU Data Protection Law
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.001.0001 · Cited by 53 · see citing works
Recent Advances in Pharmaceutical Design: Unleashing the Potential of Novel Therapeutics
Prajapati, R. N., Bhushan, B., Singh, K., Chopra, H., Kumar, S., Agrawal, M., Pathak, D., Chanchal, D. K., & Kant, L. (2024) · Current Pharmaceutical Biotechnology · doi:10.2174/0113892010275850240102105033 · Cited by 58 · see citing works
Embedding European values in data governance: a case for public data commons
Zygmuntowski, J. J., Zoboli, L., & Nemitz, P. (2021) · Internet Policy Review · doi:10.14763/2021.3.1572 · Cited by 53 · see citing works
Challenges of implementing computer-aided diagnostic models for neuroimages in a clinical setting
Leming, M., Bron, E. E., Bruffaerts, R., Ou, Y., Iglesias, J. E., Gollub, R. L., & Im, H. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00868-x · Cited by 54 · see citing works
ChatGPT in Plastic and Reconstructive Surgery
Sharma, S. C., Ramchandani, J. P., Thakker, A., & Lahiri, A. (2023) · Indian Journal of Plastic Surgery · doi:10.1055/s-0043-1771514 · Cited by 53 · see citing works
Machine learning integrated graphene oxide‐based diagnostics, drug delivery, analytical approaches to empower cancer diagnosis
Das, S., Mazumdar, H., Khondakar, K. R., & Kaushik, A. K. (2024) · BMEMat · doi:10.1002/bmm2.12117 · Cited by 52 · see citing works
From hostile worlds to multiple spheres: towards a normative pragmatics of justice for the Googlization of health
Sharon, T. (2021) · Medicine Health Care and Philosophy · doi:10.1007/s11019-021-10006-7 · Cited by 50 · see citing works
Deep learning for image analysis: Personalizing medicine closer to the point of care
Xie, Q. Y., Faust, K., Ommeren, R. V., Sheikh, A., Djuric, U., & Diamandis, P. (2019) · Critical Reviews in Clinical Laboratory Sciences · doi:10.1080/10408363.2018.1536111 · Cited by 49 · see citing works
A Review of Deep Learning for Biomedical Signals: Current Applications, Advancements, Future Prospects, Interpretation, and Challenges
Alqudah, A. M., & Moussavi, Z. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.063643 · Cited by 60 · see citing works
The role of artificial intelligence in drug screening, drug design, and clinical trials
Wu, Y., Ma, L., Li, X., Yang, J., Rao, X., Hu, Y., Xi, J., Tao, L., Wang, J., Du, L., et al. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2024.1459954 · Cited by 48 · see citing works
Artificial intelligence and machine learning algorithms
Tyagi, A. K., & Chahal, P. (2022) · Research Anthology on Machine Learning Techniques, Methods, and Applications · doi:10.4018/978-1-6684-6291-1.ch024 · Cited by 48 · see citing works
Vision: Too Little too Late? Do the Risks of FemTech already Outweigh the Benefits?
Mehrnezhad, M., Shipp, L., Almeida, T., & Toreini, E. (2022) · European Symposium on Usable Security (EuroUSEC) · doi:10.1145/3549015.3554204 · Cited by 47 · see citing works
Progress in the Development and Challenges for the Use of Artificial Kidneys and Wearable Dialysis Devices
Hueso, M., Navarro, E., Sandoval, D., & Cruzado, J. M. (2018) · Kidney Diseases · doi:10.1159/000492932 · Cited by 47 · see citing works
Privacy and Trust Redefined in Federated Machine Learning
Papadopoulos, P., Abramson, W., Hall, A. J., Pitropakis, N., & Buchanan, W. J. (2021) · Machine Learning and Knowledge Extraction · doi:10.3390/make3020017 · Cited by 50 · see citing works
Reimagining Health Data Exchange: An Application Programming Interface–Enabled Roadmap for India
Balsari, S., Fortenko, A., Blaya, J. A., Gropper, A., Jayaram, M., Matthan, R., Sahasranam, R., Shankar, M., Sarbadhikari, S. N., Bierer, B. E., et al. (2018) · Journal of Medical Internet Research · doi:10.2196/10725 · Cited by 46 · see citing works
Public Interest, Health Research and Data Protection Law: Establishing a Legitimate Trade-Off between Individual Control and Research Access to Health Data
Taylor, M., & Whitton, T. (2020) · Laws · doi:10.3390/laws9010006 · Cited by 45 · see citing works
Harmonizing regulatory regimes for the governance of patient-generated health data
Winter, J. S., & Davidson, E. (2021) · Telecommunications Policy · doi:10.1016/j.telpol.2021.102285 · Cited by 45 · see citing works
Brain Data in Context: Are New Rights the Way to Mental and Brain Privacy?
Susser, D., & Cabrera, L. Y. (2023) · AJOB Neuroscience · doi:10.1080/21507740.2023.2188275 · Cited by 45 · see citing works
Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology – national survey
Wong, K. W. Y., Gallant, F., & Szumacher, E. (2020) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2020.11.013 · Cited by 45 · see citing works
Health data justice: building new norms for health data governance
Shaw, J., & Sekalala, S. R. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00780-4 · Cited by 48 · see citing works

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 · 2015: 1
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
Social Sciences › Safety Research › 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

Integrity signals

Claims the paper makes

Claims other papers make about this one

Self-check

Cites

Cited by

Related

Record

About these checks