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Cited by 163Artificial Intelligence in Healthcare and EducationHealth Policy Implementation ScienceElectronic Health Records Systems

Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence

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Hogg, Henry David Jeffry, Al-Zubaidy, Mohaimen, Technology Enhanced Macular Services Study Reference Group, Talks, James, Denniston, Alastair K, Kelly, Christopher J, Malawana, Johann, Papoutsi, Chrysanthi +4 more·2023·Journal of Medical Internet Research
DOI 10.2196/39742PMC PMC9875023
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Abstract

Background: The rhetoric surrounding clinical artificial intelligence (AI) often exaggerates its effect on real-world care. Limited understanding of the factors that influence its implementation can perpetuate this. Objective: In this qualitative systematic review, we aimed to identify key stakeholders, consolidate their perspectives on clinical AI implementation, and characterize the evidence gaps that future qualitative research should target. Methods: Ovid-MEDLINE, EBSCO-CINAHL, ACM Digital Library, Science Citation Index-Web of Science, and Scopus were searched for primary qualitative studies on individuals’ perspectives on any application of clinical AI worldwide (January 2014-April 2021). The definition of clinical AI includes both rule-based and machine learning–enabled or non–rule-based decision support tools. The language of the reports was not an exclusion criterion. Two independent reviewers performed title, abstract, and full-text screening with a third arbiter of disagreement. Two reviewers assigned the Joanna Briggs Institute 10-point checklist for qualitative research scores for each study. A single reviewer extracted free-text data relevant to clinical AI implementation, noting the stakeholders contributing to each excerpt. The best-fit framework synthesis used the Nonadoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework. To validate the data and improve accessibility, coauthors representing each emergent stakeholder group codeveloped summaries of the factors most relevant to their respective groups. Results: The initial search yielded 4437 deduplicated articles, with 111 (2.5%) eligible for inclusion (median Joanna Briggs Institute 10-point checklist for qualitative research score, 8/10). Five distinct stakeholder groups emerged from the data: health care professionals (HCPs), patients, carers and other members of the public, developers, health care managers and leaders, and regulators or policy makers, contributing 1204 (70%), 196 (11.4%), 133 (7.7%), 129 (7.5%), and 59 (3.4%) of 1721 eligible excerpts, respectively. All stakeholder groups independently identified a breadth of implementation factors, with each producing data that were mapped between 17 and 24 of the 27 adapted Nonadoption, Abandonment, Scale-up, Spread, and Sustainability subdomains. Most of the factors that stakeholders found influential in the implementation of rule-based clinical AI also applied to non–rule-based clinical AI, with the exception of intellectual property, regulation, and sociocultural attitudes. Conclusions: Clinical AI implementation is influenced by many interdependent factors, which are in turn influenced by at least 5 distinct stakeholder groups. This implies that effective research and practice of clinical AI implementation should consider multiple stakeholder perspectives. The current underrepresentation of perspectives from stakeholders other than HCPs in the literature may limit the anticipation and management of the factors that influence successful clinical AI implementation. Future research should not only widen the representation of tools and contexts in qualitative research but also specifically investigate the perspectives of all stakeholder HCPs and emerging aspects of non–rule-based clinical AI implementation. Trial Registration: PROSPERO (International Prospective Register of Systematic Reviews) CRD42021256005; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=256005 International Registered Report Identifier (IRRID): RR2-10.2196/33145

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  • AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems.

    “Consequently, AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems [10, 11].”

    From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [11]

    In Results > Developers (page unavailable), the review says dominant electronic health record providers have driven uptake of their own clinical AI tools, and that some patients and health care organizations distrust industry. Results > Health Care Managers and Leaders (page unavailable) describes tools that failed to meet staff priorities and patient needs. These passages support concerns about industry influence and misalignment, but not the broader assertion that major technology companies lead AI development. (LLM, conf=0.84)

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Authors
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., Beyer, F. R., & Maniatopoulos, G.
Affiliations
  • Moorfields Eye Hospital NHS Foundation Trust (GB)
  • Newcastle upon Tyne Hospitals NHS Foundation Trust (GB)
  • University Hospitals Birmingham NHS Foundation Trust (GB)
  • Google (United Kingdom) (GB)
  • University of Oxford (GB)
  • Northumbria University (GB)
Corresponding author
Keane, Pearse A
ORCID
Venue
Journal of Medical Internet Research
Publisher
JMIR Publications Inc.
Published
2023-01-10
Volume
25
Pages
e39742
ISSN
1438-8871 (ISSN-L)
DOI
10.2196/39742
PMC
PMC9875023
OpenAlex
W4313060745
Cited by
163
References
145
Citations by year
2026: 58 · 2025: 53 · 2024: 41 · 2023: 11
FWCI
1.80
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Journal of Medical Internet Research (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by)
  • Northumbria Research Link (Northumbria University) (acceptedVersion)
  • UCL Discovery (University College London) (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Figshare (submittedVersion, cc-by)
  • Newcastle University ePrints (Newcastle Univesity) (cc-by)
Funders
  • Alan Turing Institute (TU/ASG/R-SPEH-113)
  • National Institute for Health and Care Research (NIHR205103, NIHR301467, NIHR205109)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › General Health Professions › Health Policy Implementation Science
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords
clinical AI, AI implementation, stakeholder perspectives, implementation factors, NASSS framework, healthcare workers, patients, caregivers, health managers, regulators, intellectual property, regulation, sociocultural attitudes, rule-based decision support, machine learning, evidence gaps, health technology adoption, implementation sustainability
MeSH terms
Machine Learning, Artificial Intelligence, Health Personnel, Humans, Qualitative Research

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