Abstract
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.
Original language | English |
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Article number | e100450 |
Pages (from-to) | 1-7 |
Number of pages | 7 |
Journal | BMJ Health and Care Informatics |
Volume | 28 |
Issue number | 1 |
DOIs | |
Publication status | Published - 1 Dec 2021 |
Bibliographical note
Copyright the Author(s) 2021. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.Keywords
- artificial intelligence
- computer-assisted
- decision making
- machine learning
- patient-centered care