Ethical Issues Raised by Artificial Intelligence and Big Data in Population Health: A Scoping Review
Open accessAbstract
Introduction: Artificial intelligence systems (AIS) powered by big data (BD) are more and more common in the healthcare sector and many anticipate that they will have a substantial effect on population health. Facing the disruptive potential of these transformations, there is a need to keep the pace with the ethical reflection accompanying the uses of AIS and the BD systems enabling such innovations. Methods: To carry out this task, we conducted a scoping review of the ethical issues of AIS and BD, in population health, based on 243 scholarly articles. Results: Our results show the explosion of publications on the subject in recent years. Our qualitative analysis of this literature highlights the potential issues of AIS and BD on the three components of population health: (1) the health outcomes and their distribution in the population and between populations; (2) the patterns of health determinants; (3) the policies and interventions developed to connect the previous components. Discussion: Our conclusions show the uncertainty of the positive outcomes of these technologies and their potential for unequal distribution. Authors consider that AIS and BD will affect determinants of health either in their understanding and by transforming the structure of these determinants. At last, this review points that the policies and interventions developed to attain population health goals will have to answer to numerous ethical expectations. This review offers a comprehensive mapping of ethical issues raised by the uses of AIS in the global field of population health.
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
Cites
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?
A scoping review of 243 articles found that AI and big data technologies reshape health determinants and policies.
“A scoping review of 243 articles found that these technologies reshape health determinants and policies [2].”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [2]
The Materials and methods section reports a final sample of 243 articles. The Results discuss how AI and big data may affect health determinants and interventions and policies, but characterize these as potential or anticipated effects rather than finding that the technologies already reshape them. Page numbers are unavailable in our copy. (LLM, conf=0.84)
Experimental — accuracy not yet measuredBig data systems influence population health outcomes by reshaping how providers understand health determinants and structure interventions.
“Big data systems influence population health outcomes by reshaping how providers understand health determinants and structure interventions [2].”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [2]
The Results (Health determinants; Health outcomes and distribution) discuss how AI and big data may affect population health, including concerns that a narrow view of health could divert attention from upstream determinants and public-health interventions. The paper does not establish that big data systems have reshaped providers’ understanding or intervention design, and it presents the effects largely as potential risks rather than demonstrated outcomes. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
PRISMA Extension for Scoping Reviews (PRISMA-ScR): Checklist and Explanation
Tricco, A. C., Lillie, E., Zarin, W., O'Brien, K. K., Colquhoun, H., Levac, D., Moher, D., Peters, M. D. J., Horsley, T., Weeks, L., et al. (2018) · Annals of Internal Medicine · doi:10.7326/m18-0850 · Cited by 43,565 · see citing works
Scoping studies: towards a methodological framework
Arksey, H., & O'Malley, L. (2005) · International Journal of Social Research Methodology · doi:10.1080/1364557032000119616 · Cited by 36,742 · see citing works
Scoping studies: advancing the methodology
Levac, D., Colquhoun, H., & O'Brien, K. K. (2010) · Implementation Science · doi:10.1186/1748-5908-5-69 · Cited by 15,268 · see citing works
Braun, V., & Clarke, V. (2012) · APA handbook of research methods in psychology, Vol 2: Research designs: Quantitative, qualitative, neuropsychological, and biological. · doi:10.1037/13620-004 · Cited by 10,299 · see citing works
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., et al. (2018) · Minds and Machines · doi:10.1007/s11023-018-9482-5 · Cited by 4,774 · see citing works
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203 · see citing works
Principles alone cannot guarantee ethical AI
Mittelstadt, B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0114-4 · Cited by 1,886 · see citing works
The Book of Why: The New Science of Cause and Effect
Goldberg, L. R. (2019) · Notices of the American Mathematical Society · doi:10.1090/noti1912 · Cited by 1,654 · see citing works
Digital technologies in the public-health response to COVID-19
Budd, J., Miller, B. S., Manning, E. M., Lampos, V., Zhuang, M., Edelstein, M., Rees, G., Emery, V. C., Stevens, M. M., Keegan, N., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-1011-4 · Cited by 1,195 · cited 6× · see citing works
“The accessibility of data can be essential for public health, and become critical during infectious outbreaks and (Budd et al., 2020; Ballantyne, 2019).”
InfluentialThe 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
Kindig, D. A., & Stoddart, G. L. (2003) · American Journal of Public Health · doi:10.2105/ajph.93.3.380 · Cited by 1,050 · see citing works
In AI we trust? Perceptions about automated decision-making by artificial intelligence
Araujo, T., Helberger, N., Kruikemeier, S., & de Vreese, C. H. (2020) · AI & SOCIETY · doi:10.1007/s00146-019-00931-w · Cited by 1,038 · see citing works
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035 · see citing works
Machine learning in medicine: Addressing ethical challenges
Vayena, E., Blasimme, A., & Cohen, I. G. (2018) · PLOS Medicine · doi:10.1371/journal.pmed.1002689 · Cited by 912 · see citing works
Artificial intelligence and the future of global health
Schwalbe, N., & Wahl, B. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30226-9 · Cited by 912 · see citing works
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882 · see citing works
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
Mohr, D. C., Zhang, M., & Schueller, S. M. (2017) · Annual Review of Clinical Psychology · doi:10.1146/annurev-clinpsy-032816-044949 · Cited by 802 · see citing works
Challenges, issues and trends in fall detection systems
Igual, R., Medrano, C., & Plaza, I. (2013) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-12-66 · Cited by 659 · see citing works
Artificial Intelligence Transforms the Future of Health Care
Noorbakhsh-Sabet, N., Zand, R., Zhang, Y., & Abedi, V. (2019) · The American Journal of Medicine · doi:10.1016/j.amjmed.2019.01.017 · Cited by 626 · see citing works
Medical big data: promise and challenges
Lee, C. H., & Yoon, H. (2017) · Kidney Research and Clinical Practice · doi:10.23876/j.krcp.2017.36.1.3 · Cited by 596 · see citing works
Interpretability of machine learning-based prediction models in healthcare
Stiglic, G., Kocbek, P., Fijacko, N., Zitnik, M., Verbert, K., & Cilar, L. (2020) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.1379 · Cited by 541 · cited 1× · see citing works
“…reliability of these systems is subject to multiple threats, including overfitting, selection bias, spurious correlations, and algorithmic opacity (Stiglic et al., 2020), which can lead to “hallucinations,” especially with large language models, which can have extremely significant impacts in…”
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
Tang, A., Tam, R. C., Cadrin-Chênevert, A., Guest, W. C., Chong, J., Barfett, J., Chepelev, L. L., Cairns, R., Mitchell, J. R., Cicero, M. D., et al. (2018) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2018.02.002 · Cited by 524 · see citing works
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
Esmaeilzadeh, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01191-1 · Cited by 507 · see citing works
The Internet of Things: Impact and Implications for Health Care Delivery
Kelly, J. T., Campbell, K. L., Gong, E., & Scuffham, P. (2020) · Journal of Medical Internet Research · doi:10.2196/20135 · Cited by 495 · see citing works
A Delphi study to build consensus on the definition and use of big data in obesity research
Vogel, C., Zwolinsky, S., Griffiths, C., Hobbs, M., Henderson, E. J., & Wilkins, E. L. (2019) · International Journal of Obesity · doi:10.1038/s41366-018-0313-9 · Cited by 452 · 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
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naudé, W. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00978-0 · Cited by 368 · see citing works
Social Media– and Internet-Based Disease Surveillance for Public Health
Aiello, A. E., Renson, A., & Zivich, P. N. (2020) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040119-094402 · Cited by 363 · see citing works
Artificial Intelligence and Big Data in Public Health
Benke, K. K., & Benke, G. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15122796 · Cited by 341 · see citing works
Big Data in Public Health: Terminology, Machine Learning, and Privacy
Mooney, S. J., & Pejaver, V. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014208 · Cited by 325 · see citing works
Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study
Castagno, S., & Khalifa, M. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.578983 · Cited by 304 · see citing works
de Graaf, M. M. A., Allouch, S. B., & Klamer, T. (2015) · Computers in Human Behavior · doi:10.1016/j.chb.2014.10.030 · Cited by 299 · see citing works
Wang, Q., Su, M., Zhang, M., & Li, R. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18116053 · Cited by 298 · see citing works
Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Cadeddu, S. B. M., Savoldelli, M., Samri, M. A., Ag Ahmed, M. A., Fleet, R., & Fortin, J.-P. (2020) · Globalization and Health · doi:10.1186/s12992-020-00584-1 · Cited by 283 · see citing works
Researching Mental Health Disorders in the Era of Social Media: Systematic Review
Wongkoblap, A., Vadillo, M. A., & Ćurčin, V. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.7215 · Cited by 269 · see citing works
Leveraging big data analytics in healthcare enhancement: trends, challenges and opportunities
Rehman, A., Naz, S., & Razzak, I. (2021) · Multimedia Systems · doi:10.1007/s00530-020-00736-8 · Cited by 269 · see citing works
Ethical challenges of big data in public health
Vayena, E., Salathé, M., Madoff, L. C., & Brownstein, J. S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1003904 · Cited by 268 · see citing works
Data, privacy, and the greater good
Horvitz, E., & Mulligan, D. (2015) · Science · doi:10.1126/science.aac4520 · Cited by 267 · see citing works
‘It's like having a physician in your pocket!’ A critical analysis of self-diagnosis smartphone apps
Lupton, D. A., & Jutel, A. (2015) · Social Science & Medicine · doi:10.1016/j.socscimed.2015.04.004 · Cited by 253 · see citing works
Nebeker, C., Torous, J., & Bartlett Ellis, R. J. (2019) · BMC Medicine · doi:10.1186/s12916-019-1377-7 · Cited by 241 · see citing works
Who Owns the Data? Open Data for Healthcare
Kostkova, P., Brewer, H., de Lusignan, S., Fottrell, E., Goldacre, B., Hart, G., Koczan, P., Knight, P., Marsolier, C., McKendry, R. A., et al. (2016) · Frontiers in Public Health · doi:10.3389/fpubh.2016.00007 · Cited by 230 · see citing works
Ethical issues in service robotics and artificial intelligence
Belk, R. (2021) · The Service Industries Journal · doi:10.1080/02642069.2020.1727892 · Cited by 228 · see citing works
Transforming Diabetes Care Through Artificial Intelligence: The Future Is Here
Dankwa-Mullan, I., Rivo, M., Sepulveda, M., Park, Y., Snowdon, J., & Rhee, K. (2019) · Population Health Management · doi:10.1089/pop.2018.0129 · Cited by 217 · see citing works
Hemingway, H., Asselbergs, F. W., Danesh, J., Dobson, R., Maniadakis, N., Maggioni, A. P., van Thiel, G. J. M. W., Cronin, M., Brobert, G., Vardas, P. E., et al. (2017) · European Heart Journal · doi:10.1093/eurheartj/ehx487 · Cited by 212 · see citing works
Big Data’s Role in Precision Public Health
Dolley, S. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00068 · Cited by 208 · see citing works
Big data hurdles in precision medicine and precision public health
Prosperi, M., Min, J. S., Bian, J., & Modave, F. (2018) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0719-2 · Cited by 200 · see citing works
Mbunge, E. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.08.029 · Cited by 199 · see citing works
Zhang, X., Pérez‐Stable, E. J., Bourne, P. E., Peprah, E. K., Duru, O. K., Breen, N., Berrigan, D. A., Wood, F. B., Jackson, J. S., Wong, D. W. S., et al. (2017) · Ethnicity & Disease · doi:10.18865/ed.27.2.95 · Cited by 194 · see citing works
Big Data and Biomedical Informatics: A Challenging Opportunity
Bellazzi, R. (2014) · Yearbook of Medical Informatics · doi:10.15265/iy-2014-0024 · Cited by 184 · see citing works
Values, challenges and future directions of big data analytics in healthcare: A systematic review
Galetsi, P., Katsaliaki, K., & Kumar, S. (2019) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.112533 · Cited by 166 · see citing works
Any Thing for Anyone? A New Digital Divide in Internet‐of‐Things Skills
van Deursen, A. J. A. M., & Mossberger, K. (2018) · Policy & Internet · doi:10.1002/poi3.171 · Cited by 161 · see citing works
Artificial Intelligence for COVID-19: Rapid Review
(2020) · doi:10.2196/21476 · Cited by 160 · see citing works
Secondary Use of Electronic Health Record: Opportunities and Challenges
Shah, S. M., & Khan, R. A. (2020) · IEEE Access · doi:10.1109/access.2020.3011099 · Cited by 158 · see citing works
Threats by artificial intelligence to human health and human existence
Federspiel, F., Mitchell, R., Asokan, A., Umana, C., & McCoy, D. (2023) · BMJ Global Health · doi:10.1136/bmjgh-2022-010435 · Cited by 156 · see citing works
Putting the data before the algorithm in big data addressing personalized healthcare
Cahan, E. M., Hernandez-Boussard, T., Thadaney-Israni, S., & Rubin, D. L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0157-2 · Cited by 149 · cited 14× · see citing works
“Biases in programming (Cahan et al., 2019; Wang and Alexander, 2020; Xie et al., 2020; Ahmed et al., 2020), for its part, may come from the amplification of previous biases and the failure to recognize them in subsequent stages (Morgenstern et al., 2021; Zou and Schiebinger, 2021; Baclic et al.,…”
InfluentialPatient privacy in the era of big data
Kayaalp, M. (2018) · Balkan Medical Journal · doi:10.4274/balkanmedj.2017.0966 · Cited by 142 · see citing works
Baclic, O., Tunis, M. C., Young, K., Doan, C., & Swerdfeger, H. (2020) · Canada Communicable Disease Report · doi:10.14745/ccdr.v46i06a02 · Cited by 136 · see citing works
An ethics framework for big data in health and research
Xafis, V., Schaefer, G. O., Labude, M. K., Brassington, I., Ballantyne, A., Lim, H. Y., Lipworth, W., Lysaght, T., Stewart, C., Sun, S., et al. (2019) · Asian Bioethics Review · doi:10.1007/s41649-019-00099-x · Cited by 136 · see citing works
Social Media, E‐Health, and Medical Ethics
Terrasse, M., Gorin, M., & Sisti, D. A. (2019) · The Hastings Center Report · doi:10.1002/hast.975 · Cited by 134 · see citing works
Big data analytics in medical engineering and healthcare: methods, advances and challenges
Wang, L., & Alexander, C. A. (2020) · Journal of Medical Engineering & Technology · doi:10.1080/03091902.2020.1769758 · Cited by 131 · see citing works
d’Elia, A., Gabbay, M., Rodgers, S., Kierans, C., Jones, E., Durrani, I., Thomas, A., & Frith, L. (2022) · Family Medicine and Community Health · doi:10.1136/fmch-2022-001670 · Cited by 129 · see citing works
Car, J., Sheikh, A., Wicks, P., & Williams, M. S. (2019) · BMC Medicine · doi:10.1186/s12916-019-1382-x · Cited by 126 · see citing works
Ensuring that biomedical AI benefits diverse populations
Zou, J., & Schiebinger, L. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103358 · Cited by 124 · see citing works
Ethical issues in using Twitter for population-level depression monitoring: a qualitative study
Mikal, J. P., Hurst, S., & Conway, M. (2016) · BMC Medical Ethics · doi:10.1186/s12910-016-0105-5 · Cited by 120 · see citing works
Social and Assistive Robotics in Dementia Care: Ethical Recommendations for Research and Practice
Ienca, M., Jotterand, F., Vică, C., & Elger, B. S. (2016) · International Journal of Social Robotics · doi:10.1007/s12369-016-0366-7 · Cited by 120 · see citing works
Advancing health equity with artificial intelligence
Thomasian, N. M., Eickhoff, C., & Adashi, E. Y. (2021) · Journal of Public Health Policy · doi:10.1057/s41271-021-00319-5 · Cited by 117 · see citing works
Morgenstern, J., Rosella, L. C., Daley, M., Goel, V., Schünemann, H. J., & Piggott, T. (2021) · BMC Public Health · doi:10.1186/s12889-020-10030-x · Cited by 112 · see citing works
Dankwa-Mullan, I., Scheufele, E. L., Matheny, M. E., Quintana, Y., Chapman, W. W., Jackson, G., & South, B. R. (2021) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2021.0065 · Cited by 108 · see citing works
Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research
Larkin, A., & Hystad, P. (2017) · Current Environmental Health Reports · doi:10.1007/s40572-017-0163-y · Cited by 106 · see citing works
COVID-19—Extending Surveillance and the Panopticon
Couch, D., Robinson, P. M., & Komesaroff, P. A. (2020) · Journal of Bioethical Inquiry · doi:10.1007/s11673-020-10036-5 · Cited by 106 · see citing works
Gathering data for decisions: best practice use of primary care electronic records for research
Canaway, R., Boyle, D. I., Manski-Nankervis, J.-A. E., Bell, J., Hocking, J. S., Clarke, K., Clark, M., Gunn, J. M., & Emery, J. D. (2019) · The Medical Journal of Australia · doi:10.5694/mja2.50026 · Cited by 103 · see citing works
Foundational Considerations for Artificial Intelligence Using Ophthalmic Images
Abràmoff, M. D., Cunningham, B., Patel, B., Eydelman, M. B., Leng, T., Sakamoto, T., Blodi, B., Grenon, S. M., Wolf, R. M., Manrai, A. K., et al. (2022) · Ophthalmology · doi:10.1016/j.ophtha.2021.08.023 · Cited by 96 · see citing works
Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data
Kim, S., Marsch, L. A., Hancock, J. T., & Das, A. K. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6426 · Cited by 94 · see citing works
Cordeiro, J. V. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.647897 · Cited by 92 · see citing works
If you build it, they will come: unintended future uses of organised health data collections
O’Doherty, K. C., Christofides, E., Yen, J., Bentzen, H. B., Burke, W., Hallowell, N., Koenig, B. A., & Willison, D. J. (2016) · BMC Medical Ethics · doi:10.1186/s12910-016-0137-x · Cited by 91 · see citing works
EULAR points to consider for the use of big data in rheumatic and musculoskeletal diseases
Gossec, L., Kedra, J., Servy, H., Pandit, A., Stones, S. R., Berenbaum, F., Finckh, A., Baraliakos, X., Stamm, T., Gómez-Cabrero, D., et al. (2019) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2019-215694 · Cited by 91 · see citing works
Why we need to be careful with LLMs in medicine
Bélisle-Pipon, J.-C. (2024) · Frontiers in Medicine · doi:10.3389/fmed.2024.1495582 · Cited by 91 · see citing works
Self-citationConway, M. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3617 · Cited by 84 · see citing works
Privacy-aware Big Data Analytics as a service for public health policies in smart cities
Anisetti, M., Ardagna, C. A., Bellandi, V., Cremonini, M., Frati, F., & Damiani, E. (2018) · Sustainable Cities and Society · doi:10.1016/j.scs.2017.12.019 · Cited by 83 · see citing works
Adjusting the focus: A public health ethics approach to data research
Ballantyne, A. (2019) · Bioethics · doi:10.1111/bioe.12551 · Cited by 82 · see citing works
Ethics, big data and computing in epidemiology and public health
Salerno, J., Knoppers, B. M., Lee, L. M., Hlaing, W. M., & Goodman, K. W. (2017) · Annals of Epidemiology · doi:10.1016/j.annepidem.2017.05.002 · Cited by 80 · see citing works
Exploring the Role of Artificial Intelligence in an Emergency and Trauma Radiology Department
Jalal, S., Parker, W., Ferguson, D., & Nicolaou, S. (2020) · Canadian Association of Radiologists Journal · doi:10.1177/0846537120918338 · Cited by 80 · see citing works
Big Data in sleep apnoea: Opportunities and challenges
Pépin, J., Bailly, S., & Tamisier, R. (2019) · Respirology · doi:10.1111/resp.13669 · Cited by 79 · see citing works
A Global Digital Citizen Science Policy to Tackle Pandemics Like COVID-19
Katapally, T. R. (2020) · Journal of Medical Internet Research · doi:10.2196/19357 · Cited by 79 · see citing works
The Promise of AI in Detection, Diagnosis, and Epidemiology for Combating COVID-19: Beyond the Hype
Abdulkareem, M., & Petersen, S. E. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.652669 · Cited by 78 · see citing works
Mind the Scales: Harnessing Spatial Big Data for Infectious Disease Surveillance and Inference
Lee, E. C., Asher, J., Goldlust, S., Kraemer, J. D., Lawson, A. B., & Bansal, S. (2016) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiw344 · Cited by 75 · see citing works
Xing, F., Peng, G., Zhang, B., Li, S., & Liang, X. (2021) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.120609 · Cited by 73 · see citing works
What Makes Artificial Intelligence Exceptional in Health Technology Assessment?
Bélisle‐Pipon, J., Couture, V., Roy, M., Ganache, I., Goetghebeur, M. M., & Cohen, I. G. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.736697 · Cited by 72 · see citing works
Self-citationTupasela, A., Snell, K., & Tarkkala, H. (2020) · Big Data & Society · doi:10.1177/2053951720907107 · Cited by 70 · see citing works
Digital health and primary care: Past, pandemic and prospects
Pagliari, C. (2021) · Journal of Global Health · doi:10.7189/jogh.11.01005 · Cited by 70 · see citing works
Detaching data from the state: Biobanking and building Big Data in Sweden
Cool, A. (2015) · BioSocieties · doi:10.1057/biosoc.2015.25 · Cited by 70 · see citing works
Australia in 2030: what is our path to health for all?
Backholer, K., Baum, F. E., Finlay, S. M., Friel, S., Giles‐Corti, B., Jones, A., Patrick, R., Shill, J., Townsend, B., Armstrong, F., et al. (2021) · The Medical Journal of Australia · doi:10.5694/mja2.51020 · Cited by 69 · see citing works
Artificial intelligence and counseling: Four levels of implementation
Fulmer, R. (2019) · Theory & Psychology · doi:10.1177/0959354319853045 · Cited by 68 · see citing works
Liu, C., & Graham, R. (2020) · Big Data & Society · doi:10.1177/2053951721995218 · Cited by 67 · see citing works
Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies
Pickering, B. (2021) · Future Internet · doi:10.3390/fi13050132 · Cited by 66 · see citing works
Developing Public Policy To Advance The Use Of Big Data In Health Care
Heitmueller, A., Henderson, S., Warburton, W., Elmagarmid, A. K., Pentland, A., & Darzi, A. W. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0771 · Cited by 63 · see citing works
Privacy and security in the big data paradigm
Sun, Z., Strang, K. D., & Pambel, F. (2018) · Journal of Computer Information Systems · doi:10.1080/08874417.2017.1418631 · Cited by 61 · see citing works
Big Data and Dementia: Charting the Route Ahead for Research, Ethics, and Policy
Ienca, M., Vayena, E., & Blasimme, A. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00013 · Cited by 61 · see citing works
Big Data and Health Care: Challenges and Opportunities for Coordinated Policy Development in the EU
Salas-Vega, S., Haimann, A., & Mossialos, E. A. (2015) · Health Systems & Reform · doi:10.1080/23288604.2015.1091538 · Cited by 60 · see citing works
Couture, V., Roy, M., Dez, E., Laperle, S., & Bélisle‐Pipon, J. (2023) · Journal of Medical Internet Research · doi:10.2196/44357 · Cited by 60 · see citing works
Self-citationThe Use and Misuse of Biomedical Data: Is Bigger Really Better?
Hoffman, S., & Podgurski, A. (2013) · American Journal of Law & Medicine · doi:10.1177/009885881303900401 · Cited by 59 · see citing works
Social Media as a Catalyst for Policy Action and Social Change for Health and Well-Being: Viewpoint
Yeung, D. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8508 · Cited by 59 · see citing works
Dahlke, D. V., & Ory, M. G. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00191 · Cited by 59 · see citing works
Cheng, C.-Y., Soh, Z. D., Majithia, S., Thakur, S., Rim, T. H., Tham, Y. C., & Wong, T. Y. (2020) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000304 · Cited by 58 · see citing works
Machine Learning and Electronic Health Records: A Paradigm Shift
Adkins, D. E. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2016.16101169 · Cited by 57 · see citing works
Ajunwa, I., Crawford, K., & Ford, J. (2016) · The Journal of Law Medicine & Ethics · doi:10.1177/1073110516667943 · Cited by 56 · see citing works
Application of Big Data analysis in gastrointestinal research
Cheung, K. S., Leung, W. K., & Seto, W. (2019) · World Journal of Gastroenterology · doi:10.3748/wjg.v25.i24.2990 · Cited by 53 · see citing works
The Digital Health Revolution and People with Disabilities: Perspective from the United States
Jones, M., DeRuyter, F., & Morris, J. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17020381 · Cited by 53 · see citing works
Beyond ‘AI for Social Good’ (AI4SG): social transformations—not tech-fixes—for health equity
Holzmeyer, C. (2021) · Interdisciplinary Science Reviews · doi:10.1080/03080188.2020.1840221 · Cited by 53 · see citing works
Nageshwaran, G., Harris, R. C., & Guerche‐Séblain, C. E. (2021) · Digital Health · doi:10.1177/20552076211002953 · Cited by 53 · see citing works
How has big data contributed to obesity research? A review of the literature
Timmins, K. A., Green, M. A., Radley, D., Morris, M. A., & Pearce, J. R. (2018) · International Journal of Obesity · doi:10.1038/s41366-018-0153-7 · Cited by 52 · see citing works
Montgomery, K. C., Chester, J., & Kopp, K. (2018) · Journal of Information Policy · doi:10.5325/jinfopoli.8.2018.0034 · Cited by 50 · see citing works
Use of big data for drug development and for public and personal health and care
Leyens, L., Reumann, M., Malats, N., & Brand, A. M. (2016) · Genetic Epidemiology · doi:10.1002/gepi.22012 · Cited by 47 · see citing works
Population health technologies
Eng, T. R. (2004) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2003.12.004 · Cited by 46 · see citing works
Why policymakers should care about “big data” in healthcare
Bates, D. W., Heitmueller, A., Kakad, M., & Saria, S. (2018) · Health Policy and Technology · doi:10.1016/j.hlpt.2018.04.006 · Cited by 46 · see citing works
Big Data and the Electronic Health Record
Peters, S. G., & Buntrock, J. D. (2014) · Journal of Ambulatory Care Management · doi:10.1097/jac.0000000000000037 · Cited by 46 · see citing works
Xie, G., Chen, T., Li, Y., Chen, T., Li, X., & Liu, Z. (2019) · Kidney Diseases · doi:10.1159/000504600 · Cited by 44 · see citing works
Communicable Disease Surveillance Ethics in the Age of Big Data and New Technology
Gilbert, G. L., Degeling, C., & Johnson, J. (2019) · Asian Bioethics Review · doi:10.1007/s41649-019-00087-1 · Cited by 42 · see citing works
The value of healthcare data: to nudge, or not?
Prainsack, B. (2020) · Policy Studies · doi:10.1080/01442872.2020.1723517 · Cited by 42 · see citing works
Baldassarre, A., Mucci, N., Padovan, M., Pellitteri, A., Viscera, S., Lecca, L. I., Galea, R. P., & Arcangeli, G. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17144975 · Cited by 42 · see citing works
Exploiting big data for critical care research
Docherty, A. B., & Lone, N. I. (2015) · Current Opinion in Critical Care · doi:10.1097/mcc.0000000000000228 · Cited by 42 · see citing works
Carney, T. J., & Kong, A. Y. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.02.011 · Cited by 41 · see citing works
Dichotomies in the Development and Implementation of Digital Mental Health Tools
Torous, J. B., & Haim, A. (2018) · Psychiatric Services · doi:10.1176/appi.ps.201800193 · Cited by 41 · see citing works
Big data and data processing in rheumatology: bioethical perspectives
de Lara, A. M., & Peláez-Ballestas, I. D. P. (2020) · Clinical Rheumatology · doi:10.1007/s10067-020-04969-w · Cited by 41 · see citing works
AI Surveillance during Pandemics: Ethical Implementation Imperatives
Shachar, C., Gerke, S., & Adashi, E. Y. (2020) · The Hastings Center Report · doi:10.1002/hast.1125 · Cited by 41 · see citing works
Tan, M., Hatef, E., Taghipour, D. J., Vyas, K., Kharrazi, H. H. K., Gottlieb, L. M., & Weiner, J. P. (2020) · JMIR Medical Informatics · doi:10.2196/18084 · Cited by 41 · see citing works
Personalizing Medicine: Disease Prevention in silico and in socio
Green, S., & Vogt, H. (2016) · BIBSYS Brage (BIBSYS (Norway)) · Cited by 40 · see citing works
Oravec, J. A. (2018) · Ethics and Social Welfare · doi:10.1080/17496535.2018.1512142 · Cited by 40 · see citing works
de Lusignan, S., Liaw, S., Kuziemsky, C. E., Mold, F., Krause, P., Fleming, D., Jones, S., & Liyanage, H. (2014) · Yearbook of Medical Informatics · doi:10.15265/iy-2014-0016 · Cited by 39 · see citing works
Participatory Disease Surveillance Systems: Ethical Framework
Geneviève, L. D., Martani, A., Wangmo, T., Paolotti, D., Koppeschaar, C. E., Kjelsø, C., Guerrisi, C., Hirsch, M., Woolley-Meza, O., Lukowicz, P., et al. (2019) · Journal of Medical Internet Research · doi:10.2196/12273 · Cited by 39 · see citing works
The imaginary of precision public health
Kenney, M., & Mamo, L. (2019) · Medical Humanities · doi:10.1136/medhum-2018-011597 · Cited by 39 · see citing works
Translational Health Disparities Research in a Data-Rich World
Breen, N., Berrigan, D. A., Jackson, J. S., Wong, D. W. S., Wood, F. B., Denny, J. C., Zhang, X., & Bourne, P. E. (2019) · Health Equity · doi:10.1089/heq.2019.0042 · Cited by 39 · see citing works
Individuals on alert: digital epidemiology and the individualization of surveillance
Samerski, S. (2018) · Life Sciences Society and Policy · doi:10.1186/s40504-018-0076-z · Cited by 38 · see citing works
Big Data, Algorithmic Governmentality and the Regulation of Pandemic Risk
Roberts, S. L. (2019) · European Journal of Risk Regulation · doi:10.1017/err.2019.6 · Cited by 38 · see citing works
Goodman, K. W. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1701966 · Cited by 38 · see citing works
Design for validation of acute myocardial infarction cases in Mini‐Sentinel
Cutrona, S. L., Toh, S., Iyer, A., Foy, S., Cavagnaro, E., Forrow, S., Racoosin, J. A., Goldberg, R. J., & Gurwitz, J. H. (2012) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.2314 · Cited by 38 · see citing works
Ethical Challenges Posed by Big Data.
Howe, E. G., & Elenberg, F. (2020) · PubMed Central · Cited by 37 · see citing works
Precision Medicine and Global Health: The Good, the Bad, and the Ugly
Mentis, A. A., Pantelidi, K., Dardiotis, E., Hadjigeorgiou, G. M., & Petinaki, E. (2018) · Frontiers in Medicine · doi:10.3389/fmed.2018.00067 · Cited by 37 · see citing works
The Emergence and Future of Public Health Data Science
Goldsmith, J., Sun, Y., Fried, L. P., Wing, J. M., Miller, G. W., & Berhane, K. T. (2021) · Public health reviews · doi:10.3389/phrs.2021.1604023 · Cited by 37 · see citing works
The rights and wrongs of robotics: Ethics and robots in public organizations
Kernaghan, K. (2014) · Canadian Public Administration · doi:10.1111/capa.12093 · Cited by 36 · see citing works
Big data in healthcare: a discussion on the big challenges
Stylianou, A., & Talias, Μ. A. (2016) · Health and Technology · doi:10.1007/s12553-016-0152-4 · Cited by 36 · see citing works
Population-Based Linkage of Big Data in Dental Research
Joda, T., Waltimo, T., Pauli‐Magnus, C., Probst-Hensch, N. M., & Zitzmann, N. U. (2018) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph15112357 · Cited by 36 · see citing works
Rennie, S., Buchbinder, M. H., Juengst, E. T., Brinkley‐Rubinstein, L., Blue, C. M., & Rosen, D. L. (2020) · Public Health Ethics · doi:10.1093/phe/phaa006 · Cited by 36 · see citing works
Diabetes and technology in 2030: a utopian or dystopian future?
Kerr, D., Axelrod, C., Hoppe, C., & Klonoff, D. C. (2018) · Diabetic Medicine · doi:10.1111/dme.13586 · Cited by 35 · see citing works
Hunt, X., Tomlinson, M., Sikander, S., Skeen, S., Marlow, M., Toit, S. D., & Eisner, M. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.543305 · Cited by 35 · see citing works
Conrad, K., Shoenfeld, Y., & Fritzler, M. J. (2020) · Autoimmunity Reviews · doi:10.1016/j.autrev.2020.102508 · Cited by 34 · see citing works
Scrutinising COVIDSafe: Frameworks for evaluating digital contact tracing technologies
Lodders, A., & Paterson, J. (2020) · Alternative Law Journal · doi:10.1177/1037969x20948262 · Cited by 33 · see citing works
Big Data for Public Health Policy-Making: Policy Empowerment
Mählmann, L., Reumann, M., Evangelatos, N. G., & Brand, A. M. (2017) · Public Health Genomics · doi:10.1159/000486587 · Cited by 32 · see citing works
Ho, C. W.-L., & Caals, K. (2021) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2021.05.009 · Cited by 32 · see citing works
Scientific challenges for precision public health
Kee, F., & Taylor-Robinson, D. C. (2020) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2019-213311 · Cited by 32 · see citing works
Big Data Warehouse for Healthcare-Sensitive Data Applications
Shahid, A., Nguyen, T.-A. N., & Kechadi, T. (2021) · Sensors · doi:10.3390/s21072353 · Cited by 31 · see citing works
A qualitative study of big data and the opioid epidemic: recommendations for data governance
Evans, E., Delorme, E., Cyr, K. D., & Goldstein, D. M. (2020) · BMC Medical Ethics · doi:10.1186/s12910-020-00544-9 · Cited by 29 · see citing works
Big Data and Public Health: Navigating Privacy Laws to Maximize Potential
Thorpe, J. H., & Gray, E. A. (2015) · Public Health Reports · doi:10.1177/003335491513000211 · Cited by 29 · see citing works
Towards responsible implementation of monitoring technologies in institutional care
Grigorovich, A., & Kontos, P. (2020) · The Gerontologist · doi:10.1093/geront/gnz190 · Cited by 29 · see citing works
Heralding the Digitalization of Life in Post-Pandemic East Asian Societies
Ho, C. W.-L., Caals, K., & Zhang, H. (2020) · Journal of Bioethical Inquiry · doi:10.1007/s11673-020-10050-7 · Cited by 29 · see citing works
Andanda, P. (2019) · GRURRR. Gewerblicher Rechtsschutz und Urheberrecht, Rechtsprechungs-Report/GRUR-DVD/GRUR-CD/IIC/Gewerblicher Rechtsschutz und Urheberrecht/Gewerblicher Rechtsschutz und Urheberrecht. Internationaler Teil · doi:10.1007/s40319-019-00873-2 · Cited by 28 · see citing works
Disambiguating the benefits and risks from public health data in the digital economy
Cheung, S. (2020) · Big Data & Society · doi:10.1177/2053951720933924 · Cited by 27 · see citing works
The Need for Developing Technology-Enabled, Safe, and Ethical Workforce for Healthcare Delivery
Sarbadhikari, S. N., & Pradhan, K. B. (2020) · Safety and Health at Work · doi:10.1016/j.shaw.2020.08.003 · Cited by 27 · see citing works
Big Data and Population Health
Hu, H., Galea, S., Rosella, L. C., & Henry, D. A. (2017) · Epidemiology · doi:10.1097/ede.0000000000000711 · Cited by 26 · see citing works
Ethics dumping in artificial intelligence
Bélisle-Pipon, J.-C., & Victor, G. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1426761 · Cited by 26 · cited 1× · see citing works
“Developers, clinicians, patients, policymakers, and civil society actors are presented not as passive recipients of ethical guidance, but as active agents responsible for aligning technological deployment with public values (Vayena et al., 2018; Bélisle-Pipon and Victor, 2024).”
Self-citationLanier, P. J., Rodriguez, M., Verbiest, S., Bryant, K., Guan, T., & Zolotor, A. (2019) · Journal of Family Violence · doi:10.1007/s10896-019-00074-y · Cited by 25 · see citing works
Health Privacy Is Difficult but Not Impossible in a Post-HIPAA Data-Driven World
Terry, N. P. (2014) · CHEST Journal · doi:10.1378/chest.13-2909 · Cited by 24 · see citing works
Defining ethical standards for the application of digital tools to population health research
Samuel, G., & Derrick, G. (2019) · Bulletin of the World Health Organization · doi:10.2471/blt.19.237370 · Cited by 24 · see citing works
Legal definitions of responsibility, accountability and liability
Cornock, M. A. (2011) · Nursing Children and Young People · doi:10.7748/ncyp2011.04.23.3.25.c8417 · Cited by 24 · see citing works
Technology, ageing and human rights: Challenges for an ageing world
Bennett, B. (2019) · International Journal of Law and Psychiatry · doi:10.1016/j.ijlp.2019.101449 · Cited by 24 · see citing works
Snell, K. (2019) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180119000057 · Cited by 23 · see citing works
Artificial intelligence and the politics of avoidance in global health
Shipton, L., & Vitale, L. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.117274 · Cited by 23 · see citing works
Governing Personalized Health: A Scoping Review
Trein, P., & Wagner, J. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.650504 · Cited by 23 · see citing works
Big Data: transforming drug development and health policy decision making
Alemayehu, D., & Berger, M. L. (2016) · Health Services and Outcomes Research Methodology · doi:10.1007/s10742-016-0144-x · Cited by 21 · see citing works
Fleming, M. N. (2020) · Ethics & Behavior · doi:10.1080/10508422.2020.1817026 · Cited by 21 · see citing works
Degeling, C., Chen, G., Gilbert, G. L., Brookes, V., Thai, T. T. H., Wilson, A. J., & Johnson, J. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-041592 · Cited by 21 · see citing works
Luk, J. W., Pruitt, L. D., Smolenski, D. J., Tucker, J., Workman, D. E., & Belsher, B. E. (2021) · Journal of Clinical Psychology · doi:10.1002/jclp.23202 · Cited by 21 · see citing works
Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process
Collins, B. X., Bélisle-Pipon, J.-C., Evans, B. J., Ferryman, K., Jiang, X., Nebeker, C., Novak, L., Roberts, K., Were, M., Yin, Z., et al. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae108 · Cited by 21 · see citing works
Self-citationCan Big Data Be Used to Monitor the Mental Health Consequences of COVID-19?
Aebi, N. J., Ridder, D. D., Ochoa, C., Petrović, D., Fadda, M., Elayan, S., Sykora, M. D., Puhan, M. A., Naslund, J. A., Mooney, S. J., et al. (2021) · International Journal of Public Health · doi:10.3389/ijph.2021.633451 · Cited by 20 · see citing works
A Linked Democracy Approach for Regulating Public Health Data
Casanovas, P., Mendelson, D., & Poblet, M. (2017) · Health and Technology · doi:10.1007/s12553-017-0191-5 · Cited by 19 · see citing works
BioMeT and Algorithm Challenges: A Proposed Digital Standardized Evaluation Framework
Godfrey, A., Goldsack, J. C., Tenaerts, P., Coravos, A., Aranda, C., Hussain, A., Barreto, M. E., Young, F., & Vitório, R. R. (2020) · IEEE Journal of Translational Engineering in Health and Medicine · doi:10.1109/jtehm.2020.2996761 · Cited by 19 · see citing works
Suicide prevention is everyone's business: Challenges and opportunities for Google
Kirtley, O. J., & O’Connor, R. C. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2019.112691 · Cited by 18 · see citing works
Jiang, H., & Cheng, L. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182010908 · Cited by 18 · see citing works
Fornasier, M. D. O. (2019) · Revista de Investigações Constitucionais · doi:10.5380/rinc.v6i2.67592 · Cited by 17 · see citing works
Bringing Big Data to Bear in Environmental Public Health: Challenges and Recommendations
Comess, S., Akbay, A., Vasiliou, M., Hines, R. N., Joppa, L., Vasiliou, V., & Kleinstreuer, N. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.00031 · Cited by 17 · see citing works
Gilbert, J.-P., Ng, V., Niu, J., & Rees, E. E. (2020) · Canada Communicable Disease Report · doi:10.14745/ccdr.v46i06a03 · Cited by 17 · see citing works
Legal, ethical, and wider implications of suicide risk detection systems in social media platforms
Celedonia, K. L., Compagnucci, M. C., Minssen, T., & Wilson, M. L. (2021) · Journal of Law and the Biosciences · doi:10.1093/jlb/lsab021 · Cited by 17 · see citing works
Implications of causality in artificial intelligence
Cavique, L. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1439702 · Cited by 17 · see citing works
van Heerden, A. C., Wassenaar, D. R., Essack, Z., Vilakazi, K., & Kohrt, B. A. (2019) · Journal of Empirical Research on Human Research Ethics · doi:10.1177/1556264619881334 · Cited by 16 · see citing works
Big Data Technology Applications and the Right to Health in China during the COVID-19 Pandemic
Shen, T., & Wang, C. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18147325 · Cited by 16 · see citing works
From Population Databases to Research and Informed Health Decisions and Policy
Machluf, Y., Tal, O., Navon, A., & Chaiter, Y. (2017) · Frontiers in Public Health · doi:10.3389/fpubh.2017.00230 · Cited by 15 · see citing works
Big Data and the Study of Social Inequalities in Health: Expectations and Issues
Delpierre, C., & Kelly‐Irving, M. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00312 · Cited by 15 · see citing works
DiiS: A Biomedical Data Access Framework for Aiding Data Driven Research Supporting FAIR Principles
Deshpande, P., Rasin, A., Furst, J. D., Raicu, D. S., & Antani, S. (2019) · Data · doi:10.3390/data4020054 · Cited by 14 · see citing works
Bose‐Brill, S., Moss, K. O., & Prater, L. (2019) · HEC Forum · doi:10.1007/s10730-019-09377-5 · Cited by 14 · see citing works
Controversies in artificial intelligence
Liu, T. Y. A., & Bressler, N. M. (2020) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000694 · Cited by 14 · see citing works
Seven Ethical Issues Affecting Neurosurgeons in the Context of Health Care Reform
Dagi, T. F. (2017) · Neurosurgery · doi:10.1093/neuros/nyx017 · Cited by 14 · see citing works
Biomedical, ethical, and moral issues being forced by advanced medical technologies.
Satava, R. M. (2003) · PubMed · Cited by 14 · see citing works
Altenburger, K. M., & Ho, D. E. (2018) · Journal of Institutional and Theoretical Economics JITE · doi:10.1628/jite-2019-0001 · Cited by 13 · see citing works
Availability, access, analysis and dissemination of small-area data
Hodgson, S., Fecht, D., Gulliver, J., Daby, H. I., Piel, F. B., Yip, F. Y., Strosnider, H. M., Hansell, A., & Elliott, P. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz051 · Cited by 13 · see citing works
The Role of Big Data in Ambient Assisted Living
Manzeschke, A., Assadi, G., & Viehöver, W. (2016) · The International Review of Information Ethics · doi:10.29173/irie149 · Cited by 13 · see citing works
Nichol, A. A., Bendavid, E., Mutenherwa, F., Patel, C. J., & Cho, M. K. (2021) · BMJ Open · doi:10.1136/bmjopen-2021-052287 · Cited by 13 · see citing works
Role of data warehousing in healthcare epidemiology
Wyllie, D. H., & Davies, J. (2015) · Journal of Hospital Infection · doi:10.1016/j.jhin.2015.01.005 · Cited by 12 · see citing works
Social Media as a New Vital Sign: Commentary
Young, S. D. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.8563 · Cited by 12 · see citing works
Ethical implications of emotion mining in medicine
Straw, I. (2020) · Health Policy and Technology · doi:10.1016/j.hlpt.2020.11.006 · Cited by 12 · see citing works
Medical research using governments’ health claims databases: with or without patients’ consent?
Tsai, F., & Junod, V. (2018) · Journal of Public Health · doi:10.1093/pubmed/fdy034 · Cited by 12 · see citing works
Flagged first, then by citations
2 of the 236 references couldn’t be matched to a known paper.
Research works printed but not linked: 4
- Balas E. A. Vernon M. Magrabi F. Gordon L. T. Sexton J. (2015). “ Big data clinical research: validity, ethics, and regulation” in Medinfo 2015 Ehealth-enabled health. eds. Sarkar I. N. Georgiou A. Marques P. M. D. (Amsterdam: Ios Press), 448– 452. 26262090
- Couture V. Bélisle-Pipon J.-C. (2023). Artificial intelligence as a threat for global health. BMJ Glob. Health. https://gh.bmj.com/content/artificial-intelligence-threat-global-health
- Leonelli S. (2019). Data-centric biology: a philosophical study. Chicago: University of Chicago Press.
- World Health Organization (Ed.) (2021). Ethics and governance of artificial intelligence for health: WHO guidance. 1st Edn. Geneva: World Health Organization.
Cited by
Sorted by citations
AI and longevity medicine: Unlocking predictive and preventive strategies for healthy aging
Green, J. B., & Haykal, D. (2026) · La Presse Médicale · doi:10.1016/j.lpm.2026.104359 · Cited by 2 · see citing works
Razzano, A. V. (2026) · Health Science Reports · doi:10.1002/hsr2.73083
Big Problems, Big Data, Big Answers
Rider, N. L. (2026) · Immunology and Allergy Clinics of North America · doi:10.1016/j.iac.2026.08.010
- Authors
- Couture, V., Roy, M.-C., Dez, E., Tremblay, F., & Bélisle-Pipon, J.-C.
- Affiliations
- Université Laval (CA)
- Université de Montréal (CA)
- Institut d'Etudes Politiques de Paris (FR)
- Corresponding author
- Couture, Vincent
- Venue
- Frontiers in Sociology
- Publisher
- Frontiers Media SA
- Published
- 2025-09-09
- Volume
- 10
- Pages
- 1536389-1536389
- ISSN
- 2297-7775 (ISSN-L)
- PMC
- PMC12454051
- OpenAlex
- W4414148133
- Cited by
- 3
- References
- 236
- Citations by year
- 2026: 3
- FWCI
- 0.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Sociology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- artificial intelligence, big data, population health, ethics, health outcomes, health disparities, health determinants, health policy, health interventions, health technology
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
