The DOI matches its Crossref record; 234 of the 236 references it cites were found in a registry or an index.
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.
Overview
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
Figures
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
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.
Citations
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
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
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
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
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
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
Principles alone cannot guarantee ethical AI
Mittelstadt, B. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0114-4 · Cited by 1,886
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 1,693
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×
“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
Kindig, D. A., & Stoddart, G. L. (2003) · American Journal of Public Health · doi:10.2105/ajph.93.3.380 · Cited by 1,050
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
Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020) · Database · doi:10.1093/database/baaa010 · Cited by 1,035
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 956
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
Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020) · Science and Engineering Ethics · doi:10.1007/s11948-019-00165-5 · Cited by 882
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
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
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
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
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 549
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×
“…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…”
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 523
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
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 478
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
Artificial intelligence vs COVID-19: limitations, constraints and pitfalls
Naudé, W. (2020) · AI & SOCIETY · doi:10.1007/s00146-020-00978-0 · Cited by 368
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
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 358
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
Wang, Q., Su, M., Zhang, M., & Li, R. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18116053 · Cited by 323
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 312
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
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
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 286
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
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
Data, privacy, and the greater good
Horvitz, E., & Mulligan, D. (2015) · Science · doi:10.1126/science.aac4520 · Cited by 267
‘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
Nebeker, C., Torous, J., & Bartlett Ellis, R. J. (2019) · BMC Medicine · doi:10.1186/s12916-019-1377-7 · Cited by 241
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
Ethical issues in service robotics and artificial intelligence
Belk, R. (2021) · The Service Industries Journal · doi:10.1080/02642069.2020.1727892 · Cited by 228
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 218
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
Big Data’s Role in Precision Public Health
Dolley, S. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00068 · Cited by 216
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
Mbunge, E. (2020) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2020.08.029 · Cited by 199
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 196
Big Data and Biomedical Informatics: A Challenging Opportunity
Bellazzi, R. (2014) · Yearbook of Medical Informatics · doi:10.15265/iy-2014-0024 · Cited by 184
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 178
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 165
Artificial Intelligence for COVID-19: Rapid Review
(2020) · doi:10.2196/21476 · Cited by 160
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
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
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×
“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.,…”
InfluentialBaclic, O., Tunis, M. C., Young, K., Doan, C., & Swerdfeger, H. (2020) · Canada Communicable Disease Report · doi:10.14745/ccdr.v46i06a02 · Cited by 144
Patient privacy in the era of big data
Kayaalp, M. (2018) · Balkan Medical Journal · doi:10.4274/balkanmedj.2017.0966 · Cited by 142
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
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
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
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 129
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
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 128
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 126
Car, J., Sheikh, A., Wicks, P., & Williams, M. S. (2019) · BMC Medicine · doi:10.1186/s12916-019-1382-x · Cited by 126
Ensuring that biomedical AI benefits diverse populations
Zou, J., & Schiebinger, L. (2021) · EBioMedicine · doi:10.1016/j.ebiom.2021.103358 · Cited by 124
Cordeiro, J. V. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.647897 · Cited by 121
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
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 117
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
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
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
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 99
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
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 94
Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data
Kim, S. J., Marsch, L. A., Hancock, J. T., & Das, A. K. (2017) · Journal of Medical Internet Research · doi:10.2196/jmir.6426 · Cited by 92
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
Self-citationConway, M. (2014) · Journal of Medical Internet Research · doi:10.2196/jmir.3617 · Cited by 84
Big Data in sleep apnoea: Opportunities and challenges
Pépin, J., Bailly, S., & Tamisier, R. (2019) · Respirology · doi:10.1111/resp.13669 · Cited by 84
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 83
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
Adjusting the focus: A public health ethics approach to data research
Ballantyne, A. (2019) · Bioethics · doi:10.1111/bioe.12551 · Cited by 82
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 82
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 82
Self-citationEthics, 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
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
Artificial intelligence and counseling: Four levels of implementation
Fulmer, R. (2019) · Theory & Psychology · doi:10.1177/0959354319853045 · Cited by 78
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
Liu, C., & Graham, R. (2020) · Big Data & Society · doi:10.1177/2053951721995218 · Cited by 73
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
Trust, but Verify: Informed Consent, AI Technologies, and Public Health Emergencies
Pickering, B. (2021) · Future Internet · doi:10.3390/fi13050132 · Cited by 71
Tupasela, A., Snell, K., & Tarkkala, H. (2020) · Big Data & Society · doi:10.1177/2053951720907107 · Cited by 70
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
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
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
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 65
Dahlke, D. V., & Ory, M. G. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00191 · Cited by 65
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
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
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 61
The 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 60
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
Ajunwa, I., Crawford, K., & Ford, J. (2016) · The Journal of Law Medicine & Ethics · doi:10.1177/1073110516667943 · Cited by 60
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 60
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
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 58
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 56
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
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
Nageshwaran, G., Harris, R. C., & Guerche‐Séblain, C. E. (2021) · Digital Health · doi:10.1177/20552076211002953 · Cited by 53
Montgomery, K. C., Chester, J., & Kopp, K. (2018) · Journal of Information Policy · doi:10.5325/jinfopoli.8.2018.0034 · Cited by 50
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 48
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
Xie, G., Chen, T., Li, Y., Chen, T., Li, X., & Liu, Z. (2019) · Kidney Diseases · doi:10.1159/000504600 · Cited by 47
Population health technologies
Eng, T. R. (2004) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2003.12.004 · Cited by 46
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
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 46
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 44
Oravec, J. A. (2018) · Ethics and Social Welfare · doi:10.1080/17496535.2018.1512142 · Cited by 43
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
The value of healthcare data: to nudge, or not?
Prainsack, B. (2020) · Policy Studies · doi:10.1080/01442872.2020.1723517 · Cited by 42
Couture, V., Roy, M.-C., Dez, E., Laperle, S., & Bélisle-Pipon, J.-C. (2022) · Journal of Medical Internet Research · doi:10.2196/44357 · Cited by 42
Self-citationExploiting 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
Carney, T. J., & Kong, A. Y. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.02.011 · Cited by 41
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
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 41
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
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
Personalizing Medicine: Disease Prevention in silico and in socio
Green, S., & Vogt, H. (2016) · BIBSYS Brage (BIBSYS (Norway)) · Cited by 40
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 40
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
The imaginary of precision public health
Kenney, M., & Mamo, L. (2019) · Medical Humanities · doi:10.1136/medhum-2018-011597 · Cited by 39
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
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
Goodman, K. W. (2020) · Yearbook of Medical Informatics · doi:10.1055/s-0040-1701966 · Cited by 38
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
Ethical Challenges Posed by Big Data.
Howe, E. G., & Elenberg, F. (2020) · PubMed Central · Cited by 37
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
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
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 37
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
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
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
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
Ho, C. W.-L., & Caals, K. (2021) · Seminars in Nephrology · doi:10.1016/j.semnephrol.2021.05.009 · Cited by 36
Conrad, K., Shoenfeld, Y., & Fritzler, M. J. (2020) · Autoimmunity Reviews · doi:10.1016/j.autrev.2020.102508 · Cited by 35
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
Scrutinising COVIDSafe: Frameworks for evaluating digital contact tracing technologies
Lodders, A., & Paterson, J. (2020) · Alternative Law Journal · doi:10.1177/1037969x20948262 · Cited by 33
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 33
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
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
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
Towards responsible implementation of monitoring technologies in institutional care
Grigorovich, A., & Kontos, P. (2020) · The Gerontologist · doi:10.1093/geront/gnz190 · Cited by 29
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
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 28
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
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
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
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
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×
“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
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
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
Fleming, M. N. (2020) · Ethics & Behavior · doi:10.1080/10508422.2020.1817026 · Cited by 24
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
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
Snell, K. (2019) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180119000057 · Cited by 23
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
Governing Personalized Health: A Scoping Review
Trein, P., & Wagner, J. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.650504 · Cited by 23
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 22
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 22
Implications of causality in artificial intelligence
Cavique, L. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1439702 · Cited by 22
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
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
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
Self-citationFrom 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 20
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 20
Can 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 19
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
Gilbert, J.-P., Ng, V., Niu, J., & Rees, E. E. (2020) · Canada Communicable Disease Report · doi:10.14745/ccdr.v46i06a03 · Cited by 18
Jiang, H., & Cheng, L. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph182010908 · Cited by 18
Fornasier, M. D. O. (2019) · Revista de Investigações Constitucionais · doi:10.5380/rinc.v6i2.67592 · Cited by 17
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
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
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
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
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
Controversies in artificial intelligence
Liu, T. Y. A., & Bressler, N. M. (2020) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000694 · Cited by 15
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
Bose‐Brill, S., Moss, K. O., & Prater, L. (2019) · HEC Forum · doi:10.1007/s10730-019-09377-5 · Cited by 14
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
Biomedical, ethical, and moral issues being forced by advanced medical technologies.
Satava, R. M. (2003) · PubMed · Cited by 14
Altenburger, K. M., & Ho, D. E. (2018) · Journal of Institutional and Theoretical Economics JITE · doi:10.1628/jite-2019-0001 · Cited by 13
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
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
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
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
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
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
Problems with research methods in medical device big data analytics
Strang, K. D. (2019) · International Journal of Data Science and Analytics · doi:10.1007/s41060-019-00176-2 · Cited by 11
Research works printed but not linked: 8
- Balas, 2015, Big data clinical research: validity, ethics, and regulation, 448
- Couture, 2023, Artificial intelligence as a threat for global health, BMJ Glob. Health
- Fjeld, 2019, Principled artificial intelligence: a map of ethical and rights-based approaches
- Leonelli, 2019, Data-centric biology: a philosophical study
- 2019, Recommendation of the council on artificial intelligence
- 2017, NVivo
- 2024, Recommendation on the ethics of artificial intelligence
- 2021, Ethics and governance of artificial intelligence for health: WHO guidance
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
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
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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
