ArticleFrontiers in digital health2022
Factors influencing clinicians' willingness to use an AI-based clinical decision support system.
Article in Frontiers in digital health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Trust in Artificial Intelligence-Based Clinical Decision Support Systems Among Health Care Workers: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Human-AI Interaction With AI-Assisted Tumor Overlays in Pediatric Whole-Body Magnetic Resonance Imaging: Exploratory Reader Study.JMIR human factors · 2026Article
- Clinicians' rationale for editing ambient AI-drafted clinical notes: persistent challenges and implications for improvement.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Exploring provider attitudes towards digital therapeutics for diabetes and depression.Scientific reports · 2026Article
- A Systematic Review of Factors Affecting Utilization of Decision Support Systems: The Interplay Between Technology, Users, and the Healthcare Environment.Health science reports · 2026Review
- Medical Practitioners' Acceptance and Use of AI-Based Clinical Decision Support Systems in Western China: A Mixed-Methods Study.Healthcare (Basel, Switzerland) · 2026Article
- Artificial intelligence in spine care: A scoping review of treatment applications.North American Spine Society journal · 2026Review
- Determinants of healthcare professionals' acceptance of DeepSeek-assisted clinical decision support applications: a cross-sectional study.BMC medical informatics and decision making · 2026Article
- Interpretability of an FDA-authorized AI/ML sepsis diagnostic tool improved by SHAP values.JAMIA open · 2026Article
- Beyond willingness: unpacking pharmacists' adoption of AI-driven clinical decision support systems through an extended UTAUT framework.Frontiers in public health · 2026Article
- Improving AI-Based Clinical Decision Support Systems and Their Integration Into Care From the Perspective of Experts: Interview Study Among Different Stakeholders.JMIR medical informatics · 2025Article
- Article
- A systematic review of clinicians' acceptance and use of clinical decision support systems over time.NPJ digital medicine · 2025Article
- User Intent to Use DeepSeek for Health Care Purposes and Their Trust in the Large Language Model: Multinational Survey Study.JMIR human factors · 2025Article
- Large language models are less effective at clinical prediction tasks than locally trained machine learning models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Investigating Clinicians' Intentions and Influencing Factors for Using an Intelligence-Enabled Diagnostic Clinical Decision Support System in Health Care Systems: Cross-Sectional Survey.Journal of medical Internet research · 2025Article
- The Hallmarks of Predictive Oncology.Cancer discovery · 2025Article
- AI-driven tools for the prediction of obesity-related vascular diseases: stakeholder perspectives and challenges.Frontiers in public health · 2025Article
- Clinical experience and perception of risk affect the acceptance and trust of using AI in medicine.Frontiers in digital health · 2025Article
- Exploring Stakeholder Perceptions about Using Artificial Intelligence for the Diagnosis of Rare and Atypical Infections.Applied clinical informatics · 2025Article
Corrections and comments
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1 author.
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Abstract
Background: Given the opportunities created by artificial intelligence (AI) based decision support systems in healthcare, the vital question is whether clinicians are willing to use this technology as an integral part of clinical workflow. Purpose: This study leverages validated questions to formulate an online survey and consequently explore cognitive human factors influencing clinicians' intention to use an AI-based Blood Utilization Calculator (BUC), an AI system embedded in the electronic health record that delivers data-driven personalized recommendations for the number of packed red blood cells to transfuse for a given patient. Method: A purposeful sampling strategy was used to exclusively include BUC users who are clinicians in a university hospital in Wisconsin. We recruited 119 BUC users who completed the entire survey. We leveraged structural equation modeling to capture the direct and indirect effects of "AI Perception" and "Expectancy" on clinicians' Intention to use the technology when mediated by "Perceived Risk". Results: The findings indicate a significant negative relationship concerning the direct impact of AI's perception on BUC Risk (ß = -0.23, Conclusion: Identifying the factors that determine clinicians' intent to use AI-based decision support systems can help improve technology adoption and use in the healthcare domain. Enhanced and safe adoption of AI can uplift the overall care process and help standardize clinical decisions and procedures. An improved AI adoption in healthcare will help clinicians share their everyday clinical workload and make critical decisions.
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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.