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

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
“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
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
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
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
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
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
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
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
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
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
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 433
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
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
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
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
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
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
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
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
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 285
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 259
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 214
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 228
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
“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 212
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
“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 203
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
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 184
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
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 205
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 231
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
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 191
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 163
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 170
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
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 150
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
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 142
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 159
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 127
Why Is the Electronic Health Record So Challenging for Research and Clinical Care?
Holmes, J., Beinlich, J., Boland, M., Bowles, K., Chen, Y., Cook, T., Demiris, G., Draugelis, M., Fluharty, L., Gabriel, P., et al. (2021) · Methods of Information in Medicine · doi:10.1055/s-0041-1731784 · Cited by 115
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
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 102
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 108
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 121
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
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 105
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
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
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 92
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
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 99
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
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
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
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 85
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
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
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 78
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
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
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 75
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
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
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 74
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 66
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 63
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 65
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
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 62
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
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 61
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
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
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
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
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 69
The Right to Erasure in EU Data Protection Law
Ausloos, J. (2020) · doi:10.1093/oso/9780198847977.001.0001 · Cited by 53
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 62
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
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 63
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 54
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
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 60
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 50
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 66
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 53
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
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
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 48
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 54
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 50
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
Harmonizing Regulatory Spheres to Overcome Challenges for Governance of Patient-Generated Health Data in the Age of Artificial Intelligence and Big Data
Winter, J. S., & Davidson, E. (2020) · Telecommunications Policy · doi:10.1016/j.telpol.2021.102285 · Cited by 60
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
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 49
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 50

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