ArticleBMJ health & care informatics2024
Achieving large-scale clinician adoption of AI-enabled decision support.
Article in BMJ health & care informatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
28 citing papers in PubMed.
- AI-Based EMG Reporting: A Randomized Controlled Trial.Journal of neurology · 2025Trial
- Clinician Trust and Human Factors in AI-Enabled Clinical Decision Support in Acute Care: Mixed Methods Study.JMIR human factors · 2026Article
- Codesign and system integration as interacting demands in digital health for youth experiencing chronic pain.Pain reports · 2026Article
- Self-Reported Knowledge, Attitudes, Perceptions, and Readiness Regarding AI Among Obstetrics and Gynecology Trainees: Cross-Sectional Study.JMIR formative research · 2026Article
- Perspectives on the Limits and Clinical Alignment of Medical AI from Population Statistics to Individual Care.Bioengineering (Basel, Switzerland) · 2026Article
- Optimising antibiotic switching via forecasting of patient physiology.Nature communications · 2026Article
- Personalised approach to hypertension treatment: protocol for the HYPERMARKER randomised controlled trial.BMJ open · 2026Article
- Uptake of Clinical Decision Support Systems Among Health Care Professionals in Six European Countries and the United States: Cross-Sectional Survey Within the I-CARE4OLD Project.Journal of medical Internet research · 2026Article
- Imaging and AI in tertiary prevention of lung cancer: Narrative review and clinical perspectives.Multidisciplinary respiratory medicine · 2026Article
- Clinical AI is Not (Yet) Trustworthy-But It Could Be.Journal of medical Internet research · 2026Article
- Knowledge, Attitudes, and Training Needs for AI in Primary Care: National Survey Study of Clinicians in the Veterans Health Administration.JMIR formative research · 2026Article
- Integrating trust into artificial intelligence for medicine: using diabetes as the exemplar disease.Journal of translational medicine · 2026Article
- The Cardiologist Driving Synthetic AI: The TIMA Method for Clinically Supervised Synthetic Data Generation.Journal of clinical medicine · 2026Article
- Perceptions and knowledge of machine learning for paediatric related decision support in emergency care - A UK and Ireland network survey study of clinician leaders.PLOS digital health · 2026Article
- A multicenter, clinically interpretable prediction model for malignancy risk in C-TIRADS 3-4 thyroid nodules.Frontiers in oncology · 2026Article
- Perceptions of healthcare professionals in rheumatology on the use of prediction models in clinical practice.Rheumatology advances in practice · 2026Article
- Preparing tomorrow's physicians for AI-driven healthcare: insights from a study on medical students', interns', and residents' knowledge, attitudes, and educational needs.Frontiers in medicine · 2026Article
- Using generative artificial intelligence in clinical practice: a narrative review and proposed agenda for implementation.The Medical journal of Australia · 2025Review
- Computational modelling of the impact of anatomical changes on ECGs in left ventricular hypertrophy.The Journal of physiology · 2025Article
- Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions.Acta pharmaceutica Sinica. B · 2025Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.