The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Implementing Ethics in Healthcare AI-Based Applications: A Scoping Review
Abstract
A number of Artificial Intelligence (AI) ethics frameworks have been published in the last 6 years in response to the growing concerns posed by the adoption of AI in different sectors, including healthcare. While there is a strong culture of medical ethics in healthcare applications, AI-based Healthcare Applications (AIHA) are challenging the existing ethics and regulatory frameworks. This scoping review explores how ethics frameworks have been implemented in AIHA, how these implementations have been evaluated and whether they have been successful. AI specific ethics frameworks in healthcare appear to have a limited adoption and they are mostly used in conjunction with other ethics frameworks. The operationalisation of ethics frameworks is a complex endeavour with challenges at different levels: ethics principles, design, technology, organisational, and regulatory. Strategies identified in this review are proactive, contextual, technological, checklist, organisational and/or evidence-based approaches. While interdisciplinary approaches show promises, how an ethics framework is implemented in an AI-based Healthcare Application is not widely reported, and there is a need for transparency for trustworthy AI.
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“Despite these practical approaches, Goirand et al [98] note that operationalizing ethical frameworks for AI is challenging, as there is a need for contextualization due to different ethical issues present in different environments.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [98]
Citations
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Background
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Cited by
Sorted by citations
Barriers to and facilitators of artificial intelligence adoption in health care: scoping review
Hassan, M., Kushniruk, A., & Borycki, E. (2024) · JMIR Human Factors · doi:10.2196/48633 · Cited by 435
Weiner, E., Dankwa‐Mullan, I., Nelson, W. A., & Hassanpour, S. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000810 · Cited by 306
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Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework
van der Vegt, A. H., Scott, I. A., Dermawan, K., Schnetler, R. J., Kalke, V. R., & Lane, P. J. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad088 · Cited by 109
Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives
Cascella, M., Schiavo, D., Cuomo, A., Ottaiano, A., Perri, F., Patrone, R., Migliarelli, S., Bignami, E. G., Vittori, A., & Cutugno, F. (2023) · Pain Research and Management · doi:10.1155/2023/6018736 · Cited by 110
Metadata
- Authors
- Goirand, M., Austin, E., & Clay-Williams, R.
- Affiliations
- Macquarie University (AU)
- Corresponding author
- Goirand, Magali
- Venue
- Science and Engineering Ethics
- Published
- 2021-09-03
- Volume
- 27
- Issue
- 5
- Pages
- 61-61
- ISSN
- 1353-3452 (ISSN-L)
- 1471-5546
- OpenAlex
- W3197709363
- Cited by
- 101
- References
- 49
- Citations by year
- 2026: 24 · 2025: 28 · 2024: 28 · 2023: 15 · 2022: 6
- FWCI
- 2.18
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science and Engineering Ethics (publishedVersion)
- PubMed (publishedVersion)
- 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 › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- AI ethics framework, artificial intelligence in healthcare, medical ethics, AI regulation, trustworthy AI, transparency, interdisciplinary approach, organizational ethics
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Ethics, Medical, Organizations, Technology
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