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Cited by 65Artificial Intelligence in Healthcare and EducationElectronic Health Records SystemsTechnology and Human Factors in Education and Health

Achieving large-scale clinician adoption of AI-enabled decision support

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Scott, Ian A., van der Vegt, Anton, Lane, Paul, McPhail, Steven, Magrabi, Farah·2024·BMJ Health & Care Informatics
DOI 10.1136/bmjhci-2023-100971PMC PMC11141172
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Abstract

Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.

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  • AI systems initially intended to lower healthcare costs often end up requiring substantial human oversight.

    “AI systems initially intended to lower healthcare costs often end up requiring substantial human oversight, illustrating the complexities involved in integrating AI effectively into clinical workflows without sacrificing clinician autonomy or patient safety [9,10].”

    From Enhancing Clinician Trust in AI Diagnostics: A Dynamic Framework for Confidence Calibration and Transparency (2025) · cites this paper as [10]

    In “The tool must not incur excessive opportunity cost or environmental impacts” (page unavailable), the paper describes costs for clinician involvement and ongoing maintenance. Its governance section also calls for ongoing monitoring and auditing. It does not say that AI systems are initially intended to lower healthcare costs or that they often end up requiring substantial human oversight. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Mixed-method evaluations show that clinicians are more confident and more likely to follow AI-generated recommendations when supported by transparent explanations such as SHAP plots.

    “Mixed-method evaluations show that clinicians are more confident and more likely to follow AI-generated recommendations when supported by transparent explanations such as SHAP plots426,427.”

    From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [426]

    In “The tool outputs must be comprehensible and actionable” (page unavailable), the paper recommends user-friendly visualisations and says explainability methods can clarify input–output relationships. It does not report mixed-method evaluations showing that explanations such as SHAP plots increase clinicians’ confidence or their likelihood of following AI recommendations; it also cautions that explanations can be misleading. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Scott, I. A., van der Vegt, A., Lane, P., McPhail, S., & Magrabi, F.
Affiliations
  • The University of Queensland (AU)
  • Digital Health Centre, The University of Queensland Faculty of Medicine and Biomedical Sciences, Herston, Queensland, Australia
  • Prince Charles Hospital (AU)
  • Queensland University of Technology (AU)
  • Macquarie University (AU)
Corresponding author
Scott, Ian A.
ORCID
Venue
BMJ Health & Care Informatics
Publisher
BMJ
Published
2024-05
Volume
31
Issue
1
Pages
e100971
ISSN
2632-1009 (ISSN-L)
DOI
10.1136/bmjhci-2023-100971
PMC
PMC11141172
OpenAlex
W4399164534
Cited by
65
References
11
Citations by year
2026: 39 · 2025: 25 · 2024: 1
FWCI
6.08
Type
journal article
Language
en
Open access
Licence
Locations
  • BMJ Health & Care Informatics (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
Keywords
artificial intelligence, computerised decision support, clinician adoption, machine learning, clinical decision-making, technology acceptance, implementation barriers, system readiness, AI adoption strategy, point-of-care devices
MeSH terms
Machine Learning, Artificial Intelligence, Australia, Humans, Decision Support Systems, Clinical

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