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Exploring stakeholder attitudes towards AI in clinical practice
Open accessAbstract
Objectives: Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out to ascertain evidence of the attitudes of clinicians, consumers, managers, researchers, regulators and industry towards AI applications in healthcare. Methods: We undertook an exploratory analysis of articles whose titles or abstracts contained the terms ‘artificial intelligence’ or ‘AI’ and ‘medical’ or ‘healthcare’ and ‘attitudes’, ‘perceptions’, ‘opinions’, ‘views’, ‘expectations’. Using a snowballing strategy, we searched PubMed and Google Scholar for articles published 1 January 2010 through 31 May 2021. We selected articles relating to non-robotic clinician-facing AI applications used to support healthcare-related tasks or decision-making. Results: Across 27 studies, attitudes towards AI applications in healthcare, in general, were positive, more so for those with direct experience of AI, but provided certain safeguards were met. AI applications which automated data interpretation and synthesis were regarded more favourably by clinicians and consumers than those that directly influenced clinical decisions or potentially impacted clinician–patient relationships. Privacy breaches and personal liability for AI-related error worried clinicians, while loss of clinician oversight and inability to fully share in decision-making worried consumers. Both clinicians and consumers wanted AI-generated advice to be trustworthy, while industry groups emphasised AI benefits and wanted more data, funding and regulatory certainty. Discussion: Certain expectations of AI applications were common to many stakeholder groups from which a set of dependencies can be defined. Conclusion: Stakeholders differ in some but not all of their attitudes towards AI. Those developing and implementing applications should consider policies and processes that bridge attitudinal disconnects between different stakeholders.
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“Similar to others, they also regarded the technology with skepticism [35,41,42].”
From An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study (2023) · cites this paper as [42]
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Metadata
- Authors
- Scott, I. A., Carter, S. M., & Coiera, E.
- Affiliations
- The University of Queensland (AU)
- University of Wollongong (AU)
- Macquarie University (AU)
- Corresponding author
- Scott, Ian A
- Venue
- BMJ Health & Care Informatics
- Publisher
- BMJ
- Published
- 2021-12
- Volume
- 28
- Issue
- 1
- Pages
- e100450
- ISSN
- 2632-1009 (ISSN-L)
- PMC
- PMC8663096
- OpenAlex
- W4200394808
- Cited by
- 193
- References
- 40
- Citations by year
- 2026: 45 · 2025: 61 · 2024: 34 · 2023: 39 · 2022: 14
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Health & Care Informatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, stakeholder attitudes, clinician attitudes, consumer attitudes, AI acceptance, clinical decision support, privacy concerns, liability for AI errors, clinician oversight, regulatory certainty, clinician-patient relationship
- MeSH terms
- Artificial Intelligence, Attitude, Delivery of Health Care, Humans, Names
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 ›
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