The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
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.
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“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
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“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
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“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]
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“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]
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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
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
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Deep Learning—A Technology With the Potential to Transform Health Care
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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
Why bioethics needs a concept of vulnerability
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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
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Items 101–101 of 101
Yıldırım, D., Kocatepe, V., Özdemir, S. T., Türkmenoğlu, A., & Önsüz, Ü. (2026) · Journal of Evaluation in Clinical Practice · doi:10.1111/jep.70606
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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
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