Ethical concerns around use of artificial intelligence in health care research from the perspective of patients with meningioma, caregivers and health care providers: a qualitative study
Open accessAbstract
BACKGROUND: As artificial intelligence (AI) approaches in research increase and AI becomes more integrated into medicine, there is a need to understand perspectives from members of the Canadian public and medical community. The aim of this project was to investigate current perspectives on ethical issues surrounding AI in health care. METHODS: In this qualitative study, adult patients with meningioma and their caregivers were recruited consecutively (August 2018-February 2019) from a neurosurgical clinic in Toronto. Health care providers caring for these patients were recruited through snowball sampling. Based on a nonsystematic literature search, we constructed 3 vignettes that sought participants' views on hypothetical issues surrounding potential AI applications in health care. The vignettes were presented to participants in interviews, which lasted 15-45 minutes. Responses were transcribed and coded for concepts, frequency of response types and larger concepts emerging from the interview. RESULTS: We interviewed 30 participants: 18 patients, 7 caregivers and 5 health care providers. For each question, a variable number of responses were recorded. The majority of participants endorsed nonconsented use of health data but advocated for disclosure and transparency. Few patients and caregivers felt that allocation of health resources should be done via computerized output, and a majority stated that it was inappropriate to delegate such decisions to a computer. Almost all participants felt that selling health data should be prohibited, and a minority stated that less privacy is acceptable for the goal of improving health. Certain caveats were identified, including the desire for deidentification of data and use within trusted institutions. INTERPRETATION: In this preliminary study, patients and caregivers reported a mixture of hopefulness and concern around the use of AI in health care research, whereas providers were generally more skeptical. These findings provide a point of departure for institutions adopting health AI solutions to consider the ethical implications of this work by understanding stakeholders' perspectives.
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?
AI presents a range of potential future concerns for qualitative research due to its rapid technological advancement, novelty, and social context.
“AI presents a range of potential future concerns for qualitative research due to its rapid technological advancement, novelty, and social context (Floridi, 2019; McCradden et al., 2020).”
From Quality Criteria: General and Specific Guidelines for Qualitative Approaches in Psychology Research. A Concise Guide for Novice Researchers and Reviewers (2024) · cites this paper as McCradden et al., 2020
The Abstract says AI is evolving rapidly and describes ethical concerns about its use in health care research, including accountability, responsibility and trust. But the paper uses qualitative interviews to study those concerns; it does not identify AI as posing future concerns for qualitative research itself or attribute such concerns to novelty and social context. The indexed passages provide no page numbers. (LLM, conf=0.86)
Experimental — accuracy not yet measuredQualitative research involving AI and human participants prompts questions about data protection, privacy, reliance on AI-generated predictions, the validity of computer outputs, commercialization of data, sharing data with private institutions, and confidentiality.
“Qualitative research involving AI and human participants prompts questions about data protection, privacy, reliance on AI-generated predictions, the validity of computer outputs, commercialization of data, sharing data with private institutions, and con fi dentiality (McCradden et al., 2020).”
From Quality Criteria: General and Specific Guidelines for Qualitative Approaches in Psychology Research. A Concise Guide for Novice Researchers and Reviewers (2024) · cites this paper as McCradden et al., 2020
There remains no consensus on the threshold of health data that requires individual consent for use, but in some cases, deidentification may be deemed satisfactory for bypassing this step.
“There remains no consensus of the threshold of health data that requires individual consent for use, but in some cases, deidentification (including of name, date of birth, address, and/or health card number) may be deemed satisfactory for bypassing this step [153].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [153]
Most patients are opposed to AI being used for allocating treatment and believe those decisions require their collaborative input.
“AI should not be used for the purposes of allocating treatment, as most patients are opposed and believe those decisions require their collaborative input [153].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [153]
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
Three Approaches to Qualitative Content Analysis
Hsieh, H.-F., & Shannon, S. E. (2005) · Qualitative Health Research · doi:10.1177/1049732305276687 · Cited by 44,601 · see citing works
How Many Interviews Are Enough?: An Experiment with Data Saturation and Variability
Guest, G., Bunce, A., & Johnson, L. (2006) · Field Methods · doi:10.1177/1525822x05279903 · Cited by 20,808 · see citing works
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works
Implementing Machine Learning in Health Care — Addressing Ethical Challenges
Char, D. S., Shah, N. H., & Magnus, D. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1714229 · Cited by 1,857 · see citing works
Bansal, G., Zahedi, F. “., & Gefen, D. (2010) · Decision Support Systems · doi:10.1016/j.dss.2010.01.010 · Cited by 958 · see citing works
Deep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667 · see citing works
Hengstler, M., Enkel, E., & Duelli, S. (2016) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2015.12.014 · Cited by 661 · see citing works
The Limitations of “Vulnerability” as a Protection for Human Research Participants
Levine, C., Faden, R., Grady, C., Hammerschmidt, D., Eckenwiler, L., & Sugarman, J. (2004) · The American Journal of Bioethics · doi:10.1080/15265160490497083 · Cited by 438 · see citing works
Why bioethics needs a concept of vulnerability
Rogers, W., Mackenzie, C., & Dodds, S. (2012) · International Journal of Feminist Approaches to Bioethics · doi:10.3138/ijfab.5.2.11 · Cited by 333 · see citing works
Computer knows best? The need for value-flexibility in medical AI
McDougall, R. J. (2019) · Journal of Medical Ethics · doi:10.1136/medethics-2018-105118 · Cited by 262 · see citing works
On the Prospects for a (Deep) Learning Health Care System
Naylor, C. D. (2018) · JAMA · doi:10.1001/jama.2018.11103 · Cited by 250 · see citing works
Bioethics, Vulnerability, and Protection
Macklin, R. (2003) · Bioethics · doi:10.1111/1467-8519.00362 · Cited by 210 · see citing works
Robling, M. R., Hood, K., Houston, H., Pill, R., Fay, J., & Evans, H. M. (2004) · Journal of Medical Ethics · doi:10.1136/jme.2003.005157 · Cited by 127 · see citing works
Patient Perspectives About Decisions to Share Medical Data and Biospecimens for Research
Kim, J., Kim, H., Bell, E., Bath, T., Paul, P., Pham, A., Jiang, X., Zheng, K., & Lucila Ohno-machado, A. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.9550 · Cited by 104 · see citing works
Humanizing Artificial Intelligence
Israni, S. T., & Verghese, A. (2018) · JAMA · doi:10.1001/jama.2018.19398 · Cited by 94 · see citing works
Gaylin, D. S., Moiduddin, A., Mohamoud, S., Lundeen, K., & Kelly, J. A. (2011) · Health Services Research · doi:10.1111/j.1475-6773.2010.01233.x · Cited by 78 · see citing works
Paprica, P. A., de Melo, M. N., & Schull, M. J. (2019) · CMAJ Open · doi:10.9778/cmajo.20180099 · Cited by 57 · see citing works
Aicken, C. R. H., Fuller, S., Sutcliffe, L. J., Estcourt, C. S., Gkatzidou, V., Oakeshott, P., HONE, K. S., Sadiq, S. T., Sonnenberg, P., & Shahmanesh, M. (2016) · BMC Public Health · doi:10.1186/s12889-016-3648-y · Cited by 56 · see citing works
Willison, D. J., Swinton, M. E., Schwartz, L. J., Abelson, J., Charles, C. A., Northrup, D., Cheng, J., & Thabane, L. (2008) · BMC Medical Ethics · doi:10.1186/1472-6939-9-18 · Cited by 55 · see citing works
Schulz, A. J., Caldwell, C. H., & Foster, S. (2003) · Health Education & Behavior · doi:10.1177/1090198102251026 · Cited by 55 · see citing works
The battle for ethical AI at the world’s biggest machine-learning conference
Gibney, E. (2020) · Nature · doi:10.1038/d41586-020-00160-y · Cited by 54 · see citing works
Haddow, G., Bruce, A., Sathanandam, S., & Wyatt, J. C. (2010) · Journal of Evaluation in Clinical Practice · doi:10.1111/j.1365-2753.2010.01488.x · Cited by 50 · see citing works
The Importance of Purpose: Moving Beyond Consent in the Societal Use of Personal Health Information
Grande, D., Mitra, N., Shah, A., Wan, F., & Asch, D. A. (2014) · Annals of Internal Medicine · doi:10.7326/m14-1118 · Cited by 49 · see citing works
Lehnbom, E. C., Brien, J. E., & McLachlan, A. J. (2014) · Internal Medicine Journal · doi:10.1111/imj.12384 · Cited by 28 · see citing works
Xu, J. C., Bryant, D. G., & Howard, A. M. (2018) · IEEE RO-MAN · doi:10.1109/roman.2018.8525782 · Cited by 26 · see citing works
Determining the unmet needs of patients with intracranial meningioma—a qualitative assessment
Baba, A., McCradden, M. D., Rabski, J., & Cusimano, M. D. (2019) · Neuro-Oncology Practice · doi:10.1093/nop/npz054 · Cited by 16 · see citing works
Self-citationFamily Perspectives on Integrated Child Health Information Systems
Hastings, T. M. (2004) · Journal of Public Health Management and Practice · doi:10.1097/00124784-200411001-00004 · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review
Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, D., Biessmann, F., & Wolf-Ostermann, K. (2021) · Journal of Medical Internet Research · doi:10.2196/26522 · Cited by 314 · see citing works
Karimian, G., Petelos, E., & Evers, S. (2022) · AI and Ethics · doi:10.1007/s43681-021-00131-7 · Cited by 306 · see citing works
Perceptions and Needs of Artificial Intelligence in Health Care to Increase Adoption: Scoping Review
Chew, H. S. J., & Achananuparp, P. (2022) · Journal of Medical Internet Research · doi:10.2196/32939 · Cited by 243 · see citing works
Exploring stakeholder attitudes towards AI in clinical practice
Scott, I. A., Carter, S. M., & Coiera, E. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100450 · Cited by 193 · see citing works
Medical artificial intelligence ethics: A systematic review of empirical studies
Tang, L., Li, J., & Fantus, S. (2023) · Digital Health · doi:10.1177/20552076231186064 · Cited by 199 · see citing works
Hogg, H. D. J., Al-Zubaidy, M., Technology Enhanced Macular Services Study Reference Group, Talks, J., Denniston, A. K., Kelly, C. J., Malawana, J., Papoutsi, C., Teare, M. D., Keane, P. A., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/39742 · Cited by 163 · see citing works
Artificial Intelligence for COVID-19: Rapid Review
(2020) · doi:10.2196/21476 · Cited by 160 · see citing works
Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers
Solanki, P., Grundy, J., & Hussain, W. (2023) · AI and Ethics · doi:10.1007/s43681-022-00195-z · Cited by 157 · see citing works
“There remains no consensus of the threshold of health data that requires individual consent for use, but in some cases, deidentification (including of name, date of birth, address, and/or health card number) may be deemed satisfactory for bypassing this step [153].”
BackgroundAggarwal, R., Farag, S., Martin, G., Ashrafian, H., & Darzi, A. W. (2021) · Journal of Medical Internet Research · doi:10.2196/26162 · Cited by 135 · see citing works
A Research Ethics Framework for the Clinical Translation of Healthcare Machine Learning
McCradden, M. D., Anderson, J. A., Stephenson, E. A., Drysdale, E., Erdman, L., Goldenberg, A., & Shaul, R. Z. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2021.2013977 · Cited by 117 · see citing works
Self-citationPatient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review
Moy, S., Irannejad, M., Manning, S. J., Farahani, M., Ahmed, Y., Gao, E., Prabhune, R., Lorenz, S., Mirza, R. M., & Klinger, C. A. (2024) · Journal of patient-centered research and reviews · doi:10.17294/2330-0698.2029 · Cited by 113 · see citing works
Specific challenges posed by artificial intelligence in research ethics
Bouhouita-Guermech, S., Gogognon, P., & Bélisle‐Pipon, J. (2023) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1149082 · Cited by 97 · see citing works
McCradden, M. D., Sarker, T., & Paprica, P. A. (2020) · BMJ Open · doi:10.1136/bmjopen-2020-039798 · Cited by 91 · see citing works
Self-citationWu, C., Xu, H., Bai, D., Chen, X., Gao, J., & Jiang, X. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-066322 · Cited by 81 · see citing works
Pedro, A. R., Dias, M. B., Laranjo, L., Cunha, A. S., & Cordeiro, J. V. (2023) · PLOS ONE · doi:10.1371/journal.pone.0290613 · Cited by 74 · see citing works
Gundlack, J., Thiel, C., Negash, S., Buch, C., Apfelbacher, T., Denny, K., Christoph, J., Mikolajczyk, R., Unverzagt, S., & Frese, T. (2025) · Journal of Medical Internet Research · doi:10.2196/70487 · Cited by 60 · see citing works
Balancing AI and academic integrity: what are the positions of academic publishers and universities?
Gulumbe, B. H., Audu, S. M., & Hashim, A. M. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-01946-8 · Cited by 53 · see citing works
Thai, K., Tsiandoulas, K., Stephenson, E. A., Menna‐Dack, D., Shaul, R. Z., Anderson, J. A., Shinewald, A., Ampofo, A., & McCradden, M. D. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.10659 · Cited by 54 · see citing works
Self-citationEthical implementation of artificial intelligence in the service industries
Vatankhah, S., Bamshad, V., Arıcı, H. E., & Duan, Y. (2024) · Service Industries Journal · doi:10.1080/02642069.2024.2359077 · Cited by 57 · see citing works
Cena, E., Brooks, J., Day, W., Goodman, S., Rousaki, A., Ruby-Granger, V., & Seymour-Smith, S. (2024) · International Journal of Qualitative Methods · doi:10.1177/16094069241282843 · Cited by 49 · see citing works
“Examples of applied research in this domain include AI and human collaboration in vehicle management (Xu et al., 2023) and healthcare (McCradden et al., 2020).”
Publics’ views on ethical challenges of artificial intelligence: a scoping review
Machado, H., Silva, S., & Neiva, L. (2023) · AI and Ethics · doi:10.1007/s43681-023-00387-1 · Cited by 60 · see citing works
Türkiye ve Dünyada Sağlık Hizmetlerinde Yapay Zekâ
TARCAN, G. Y., BALÇIK, P. Y., & Sebik, N. B. (2023) · Mersin Üniversitesi Tıp Fakültesi Lokman Hekim Tıp Tarihi ve Folklorik Tıp Dergisi · doi:10.31020/mutftd.1278529 · Cited by 40 · see citing works
Li, L. T., Haley, L. C., Boyd, A. K., & Bernstam, E. V. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104531 · Cited by 45 · see citing works
Khan, S. D., Hoodbhoy, Z., Raja, M. H. R., Kim, J. Y., Hogg, H. D. J., Manji, A. A. A., Gulamali, F., Hasan, A., Shaikh, A., Tajuddin, S., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000514 · Cited by 36 · see citing works
Sahoo, R. K., Sahoo, K. C., Negi, S., Baliarsingh, S. K., Panda, B., & Pati, S. (2025) · Patient Education and Counseling · doi:10.1016/j.pec.2025.108680 · Cited by 40 · see citing works
Cumyn, A., Ménard, J., Barton, A., Dault, R., Lévesque, F., & Éthier, J. (2023) · Journal of Medical Internet Research · doi:10.2196/45002 · Cited by 35 · see citing works
Guidelines for Conducting Ethical Artificial Intelligence Research in Neurology
Chiang, S., Picard, R. W., Chiong, W., Moss, R., Worrell, G. A., Rao, V. R., & Goldenholz, D. M. (2021) · Neurology · doi:10.1212/wnl.0000000000012570 · Cited by 75 · see citing works
Ramessur, R., Raja, L., Kilduff, C. L. S., Kang, S., Li, J.-P. O., Thomas, P. B. M., & Sim, D. A. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000406 · Cited by 34 · see citing works
Scheibner, J., Ienca, M., & Vayena, E. (2022) · BMC Medical Ethics · doi:10.1186/s12910-022-00852-2 · Cited by 33 · see citing works
Jeyakumar, T., Younus, S., Zhang, M., Clare, M., Charow, R., Karsan, I., Dhalla, A., Al-Mouaswas, D., Scandiffio, J., Aling, J., et al. (2023) · JMIR AI · doi:10.2196/40973 · Cited by 33 · see citing works
Vervoort, D., Tam, D. Y., & Wijeysundera, H. C. (2021) · Canadian Journal of Cardiology · doi:10.1016/j.cjca.2021.08.015 · Cited by 34 · see citing works
Vishwanatha, J. K., Christian, A. H., Sambamoorthi, U., Thompson, E. L., Stinson, K., & Syed, T. A. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000288 · Cited by 29 · see citing works
Lysen, F., & Wyatt, S. (2024) · Journal of Responsible Innovation · doi:10.1080/23299460.2023.2300161 · Cited by 32 · see citing works
Gould, D. J., Dowsey, M. M., Glanville-Hearst, M., Spelman, T. D., Bailey, J., Choong, P., & Bunzli, S. (2023) · Journal of Medical Internet Research · doi:10.2196/43632 · Cited by 27 · see citing works
Maris, M. T., Koçar, A., Willems, D. L., Pols, J. J., Tan, H. L., Lindinger, G. L., & Bak, M. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01042-y · Cited by 30 · see citing works
The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making
Macri, R., & Roberts, S. L. (2023) · Current Oncology · doi:10.3390/curroncol30020168 · Cited by 29 · see citing works
A Process Framework for Ethically Deploying Artificial Intelligence in Oncology
Hantel, A., Clancy, D. D., Kehl, K. L., Marron, J. M., Allen, E. M. V., & Abel, G. A. (2022) · Journal of Clinical Oncology · doi:10.1200/jco.22.01113 · Cited by 27 · see citing works
Ethical aspects of AI use in the circular economy
Bashynska, I. (2025) · AI & Society · doi:10.1007/s00146-025-02436-1 · Cited by 48 · see citing works
Windisch, P. Y., Koechli, C., Rogers, S. J., Schröder, C., Forster, R. E., Zwahlen, D. R., & Bodis, S. B. (2022) · Cancers · doi:10.3390/cancers14112676 · Cited by 21 · see citing works
Alzahrani, A., Robinson, S., & Ahmad, M. (2022) · International Conference on Human-Agent Interaction (HAI) · doi:10.1145/3527188.3561920 · Cited by 21 · see citing works
Kim, J. P., Ryan, K., Kasun, M., Hogg, J., Dunn, L. B., & Roberts, L. W. (2023) · JMIR AI · doi:10.2196/47449 · Cited by 18 · see citing works
Mwogosi, A. (2025) · Digital Health · doi:10.1177/20552076251344385 · Cited by 18 · see citing works
Feng, Y., Leung, A. A. C., Lu, X., Liang, Z., Quan, H., & Walker, R. L. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01814-3 · Cited by 17 · see citing works
Kelly, B. S., Kirwan, A., Quinn, M. S., Kelly, A. M., Mathur, P., Lawlor, A., & Killeen, R. P. (2023) · Radiography · doi:10.1016/j.radi.2023.03.009 · Cited by 17 · see citing works
Winter, P., & Carusi, A. (2022) · Journal of Responsible Technology · doi:10.1016/j.jrt.2022.100052 · Cited by 17 · see citing works
Navarro-Ballester, A. (2024) · NeuroMarkers. · doi:10.1016/j.neumar.2024.100033 · Cited by 16 · see citing works
A Comprehensive Review of the Impact of Machine Learning and Omics on Rare Neurological Diseases
Alganmi, N. A. (2024) · BioMedInformatics · doi:10.3390/biomedinformatics4020073 · Cited by 15 · see citing works
Grauman, Å., Ancillotti, M., Veldwijk, J., & Mascalzoni, D. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02395-x · Cited by 16 · see citing works
Schmidt, E., Putora, P. M., & Fijten, R. (2025) · Philosophy & Technology · doi:10.1007/s13347-024-00834-9 · Cited by 14 · see citing works
“Indeed, we think that it may be par - tially responsible for a certain skepticism and hesitancy towards AI in the healthcare domain (Hengstler et al., 2016 ; Nadarzynski et al., 2019 ; McCradden et al. 2020 ).”
Ho, A., Bavli, I., Mahal, R., & McKeown, M. J. (2023) · AJOB Empirical Bioethics · doi:10.1080/23294515.2023.2274582 · Cited by 12 · see citing works
Zhang, X., Hu, J., & Zhou, Y. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13535-3 · Cited by 16 · see citing works
Patient Attitudes Toward Artificial Intelligence in Cancer Care: Scoping Review
Hilbers, D., Nekain, N., Bates, A. T., & Nuñez, J.-J. (2025) · JMIR Cancer · doi:10.2196/74010 · Cited by 13 · see citing works
Inalienable data: Ethical imaginaries of de-identified health data ownership
McKay, F., Treanor, D. E., & Hallowell, N. (2023) · SSM - Qualitative Research in Health · doi:10.1016/j.ssmqr.2023.100321 · Cited by 12 · see citing works
Labib, K., Ghumman, H., Jain, S., & Jarstad, J. S. (2024) · Cureus · doi:10.7759/cureus.71063 · Cited by 12 · see citing works
Optimizing clinical research procedures in public health emergencies
Madariaga, A., Kasherman, L., Karakasis, K., Degendorfer, P., Heesters, A. M., Xu, W., Husain, S., & Oza, A. M. (2020) · Medicinal Research Reviews · doi:10.1002/med.21749 · Cited by 10 · see citing works
Lee, J., Jung, K., Newman, E., Chow, E., & Chen, Y. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713244 · Cited by 9 · see citing works
Weinert, L., Klass, M., Schneider, G., & Heinze, O. (2023) · JMIR Formative Research · doi:10.2196/43958 · Cited by 8 · see citing works
Cavallucci, M., Andalò, A., Danesi, V., Gentili, N., Massa, I., Scarpi, E., Restuccia, M., Vespignani, R., Conficconi, A., Palleschi, M., et al. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106237 · Cited by 7 · see citing works
Hou, J., Zhang, Z., Cheng, X., & Wang, W. (2026) · Journal of Medical Internet Research · doi:10.2196/85663 · Cited by 8 · see citing works
Koechli, C., Zwahlen, D. R., Schucht, P., & Windisch, P. Y. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.110866 · Cited by 7 · see citing works
Espinosa-Sánchez, A., & Carnero, A. (2026) · Cells · doi:10.3390/cells15111031 · Cited by 6 · see citing works
Aldossari, M. (2023) · Asian Journal of Science and Applied Technology · doi:10.51983/ajsat-2023.12.2.3973 · Cited by 6 · see citing works
Utilisations secondaires des données de santé : impacts de la transparence
Bilodeau, E., Cumyn, A., Ménard, J., Barton, A., Dault, R., & Éthier, J. (2024) · Canadian Journal of Bioethics · doi:10.7202/1112284ar · Cited by 5 · see citing works
Regulatory Strategies for Fostering Responsible AI Innovation in African Economies
Shao, D. D., Marwa, N. W., & Didas, M. (2023) · International Conference on the Advancements of Artificial Intelligence in African Context (AAIAC) · doi:10.1109/aaiac60008.2023.10465404 · Cited by 4 · see citing works
Ehlert, H., & Lüdtke, U. M. (2025) · International Journal of Language & Communication Disorders · doi:10.1111/1460-6984.70172 · Cited by 4 · see citing works
Sounderajah, V., Normahani, P., Aggarwal, R., Jayakumar, S., Markar, S. R., Ashrafian, H., & Darzi, A. W. (2022) · Springer eBooks · doi:10.1007/978-3-030-64573-1_34 · Cited by 3 · see citing works
Partnering with children and youth to advance artificial intelligence in healthcare
McCradden, M. D. (2022) · Pediatric Research · doi:10.1038/s41390-022-02139-z · Cited by 3 · see citing works
Self-citationChourasia, P. K., Coffel, M., Conklin, J., Wood, W. A., & Khairat, S. (2025) · Journal of Patient Experience · doi:10.1177/23743735251380949 · Cited by 2 · see citing works
Patient Perceptions of the Use of Artificial Intelligence in Radiology: A Scoping Review
Udupa, S., Sharma, O., Sharma, S., & Yong-Hing, C. J. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.01.001 · Cited by 3 · see citing works
Ravikumar, R. N., & Aarthi, S. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-7620-2.ch003 · Cited by 2 · see citing works
Response to Open Peer Commentaries: On Social Harms, Big Tech, and Institutional Accountability
Anderson, J. A., McCradden, M. D., & Stephenson, E. A. (2022) · The American Journal of Bioethics · doi:10.1080/15265161.2022.2075977 · Cited by 2 · see citing works
Self-citationDeclaration of Computational Neurosurgery
Ieva, A. D., Molina, E. S., Somerville, M. A., Beheshti, A., Staartjes, V. E., Serra, C., Theodore, N., Elliott, J. M., Wesselink, E. O., Russo, C., et al. (2024) · Advances in experimental medicine and biology · doi:10.1007/978-3-031-64892-2_2 · Cited by 2 · see citing works
Self-citationEthical Considerations in AI-Powered Health Communities to Promote Good Health and Wellbeing
Ang, C., & Lee, K.-F. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-5837-5.ch003 · Cited by 2 · see citing works
Public perceptions and implementation considerations on the use of artificial intelligence in health
Romero, R. A., & Young, S. D. (2021) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.13580 · Cited by 2 · see citing works
Awareness About Artificial Intelligence Among Dental Practitioners And Dental Students
Gokul, R. N., K, M., K, S., & V, P. (2024) · JOURNAL OF CLINICAL PROSTHODONTICS AND IMPLANTOLOGY · doi:10.55995/j-cpi.2024013 · Cited by 1 · see citing works
AI in Social Worker Recruitment and Training for Bullying Intervention
Maota, Y.-Y., & Maota, T. (2025) · IGI Global eBooks · doi:10.4018/979-8-3693-8679-8.ch001 · Cited by 1 · see citing works
GADDAM, M., GULLAPALLI, D., Adrish, Z. A., Reddy, A. Y., & Adrish, M. (2025) · World Journal of Critical Care Medicine · doi:10.5492/wjccm.v14.i3.108272 · Cited by 1 · see citing works
Using AI to Improve Emergency Care: A Detailed Study of Benefits, Challenges and What's Next
Sen, A. A. A., Alshehri, A. M. M., Tayan, O., Alkhodre, A. B., Bahbouh, N. M., & Alfarag, F. (2025) · International Conference on Computing for Sustainable Global Development · doi:10.23919/indiacom66777.2025.11115299 · Cited by 2 · see citing works
Potas, N., ERKILIÇ, C. E., Caner, S., & Fritsch, S. J. (2026) · Work · doi:10.1177/10519815261429412 · Cited by 1 · see citing works
AI-Enhanced Health Counseling: A Futuristic Approach to Holistic Well-Being
Romero, C. B., & Subardjo, R. Y. S. (2024) · Bincang Sains dan Teknologi · doi:10.56741/bst.v3i03.631 · Cited by 1 · see citing works
Naidu, G., & Krishnan, V. (2025) · International Journal of Intelligence Science · doi:10.4236/ijis.2025.151002 · Cited by 3 · see citing works
Sounderajah, V., Normahani, P., Aggarwal, R., Jayakumar, S., Markar, S. R., Ashrafian, H., & Darzi, A. W. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_34-1
McCradden, M. D. (2023) · The American Journal of Bioethics · doi:10.1080/15265161.2023.2237459
Self-citationVincy, S. J. (2024) · Journal of chemical health risks · doi:10.52783/jchr.v14.i2.3660
Verma, M., Mirza, M., Sayal, K., Shenoy, S., Sahoo, S. S., Goel, A., & Kakkar, R. (2025) · The Indian Journal of Medical Research · doi:10.25259/ijmr_337_2024
Patient Attitudes Toward Artificial Intelligence in Cancer Care: A Scoping ReviewPreprint
Hilbers, D., Nekain, N., Bates, A. T., & Nuñez, J.-J. (2025) · medRxiv · doi:10.1101/2025.03.15.25324029
Ethical concerns in healthcare analytics: exploring the complexities of data-driven decision making
Rajak, M. K., & Dey, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-26480-1.00012-6
Ethical concern of data privacy and patient data ownership
Gaur, L., & Abraham, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33124-4.00014-x
Ethical concerns of generative AI in healthcare applications
Gaur, L., & Abraham, A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33124-4.00009-6
Artificial Intelligence Applications in Global Surgery and Surgical Nursing
Yeşilyurt, K. Ö. (2025) · Hemşirelik Bilimi Dergisi · doi:10.54189/hbd.1606325
Beyond the Hype: Risks, Realism, and Responsibilities in Digital Twins for Alzheimer’s Disease
Guttmann-Flury, E., Zhao, J., & Sawan, M. (2026) · Artificial intelligence · doi:10.5772/intechopen.1014167
Verma, M., & Beg, M. J. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1325-2.ch003
Artificial Intelligence in Health Care: Ethical Issues and Challenges
Ikram, M., Bkiyar, H., Markaoui, K., Abdelkrim, D., & Brahim, H. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18716-1_41
Assessing the Reliability of Artificial Intelligence in the Healthcare Sector
Upadhayay, S., Verma, D., Juyal, S., & Jain, V. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-8889-2.ch006
A maturity model framework for federated networks of trusted research environments
Luong, J. H. C., Beggs, J., Ferris, J., Furuta, K., Gorse, A., Jefferson, E., Aung, K. M. M., Sullivan, C. M., Woo, X. Y., Cheong, I. H., et al. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1699125
Arık, Ş. B., & Güven, G. Ö. (2026) · Mersin Üniversitesi Tıp Fakültesi Lokman Hekim Tıp Tarihi ve Folklorik Tıp Dergisi · doi:10.31020/mutftd.1853893
Bertók, T., Ivana, P., Jáné, E., Zuzana, B., & Jan, T. (2026) · Clinica Chimica Acta · doi:10.1016/j.cca.2026.121209
Continuous assurance for AI-driven clinical decision support systems
Al‐Horani, R. A., & Tadesse, A. F. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1870819
Balancing technology and ethics in the transformation of modern hospital healthcare
Yusof, M. S., & Wahab, N. S. A. (2021) · The Asian Journal of Professional and Business Studies/The Asian Journal of Professional & Business Studies · doi:10.61688/ajpbs.v2i1.146
Yousefi, R., Charow, R., Scane, K., Jaffer, A. M., Kim, H., McClure, S., Mohan, A., Abdulrahman, S. T., Williams, L., & Wiljer, D. (2026) · doi:10.2196/preprints.110832
- Authors
- McCradden, M. D., Baba, A., Saha, A., Ahmad, S., Boparai, K., Fadaiefard, P., & Cusimano, M. D.
- Affiliations
- St. Michael's Hospital (CA)
- Venue
- CMAJ Open
- Publisher
- CMA Impact Inc.
- Published
- 2020-01
- Volume
- 8
- Issue
- 1
- Pages
- E90-E95
- ISSN
- 2291-0026 (ISSN-L)
- PMC
- PMC7028163
- OpenAlex
- W3007819350
- Cited by
- 101
- References
- 27
- Citations by year
- 2026: 15 · 2025: 22 · 2024: 15 · 2023: 23 · 2022: 14 · 2021: 9 · 2020: 3
- FWCI
- 3.63
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- CMAJ Open (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics in Clinical Research
- Keywords
- artificial intelligence in healthcare, AI ethics, health data privacy, healthcare resource allocation, algorithmic decision-making, transparency, caregivers, healthcare workers, public attitudes toward AI
- MeSH terms
- Adult, Aged, Aged, 80 and over, Artificial Intelligence, Canada, Ethics, Medical, Female, Health Personnel, Health Services Research, Humans, Male, Meningioma, Middle Aged, Caregivers, Qualitative Research, Young Adult
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
