ArticlePLOS digital health2025
Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts.
Article in PLOS digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The trial behind it
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Who cites it
11 citing papers in PubMed.
- Patients' and Physicians' Perceptions of AI Integration in Prostate Cancer Diagnosis: Mixed Methods Study of Challenges to the Patient-Physician Relationship.Journal of medical Internet research · 2026Article
- AI for Health Care Quality and Patient Safety: Scoping Review of Diagnostic, Predictive, and Decision Support Applications.Journal of medical Internet research · 2026Article
- AI Adoption in US Cancer Centers: National Cross-Sectional Study of Institutional and Policy Determinants.Journal of medical Internet research · 2026Article
- Operationalizing the EU AI Act in a comprehensive cancer center through an institutional governance framework.NPJ digital medicine · 2026Review
- The Design-Driven Innovation Path of Human-Centered Artificial Intelligence in the Field of Healthcare: Theory, Practice, and Future Prospects.Healthcare (Basel, Switzerland) · 2026Review
- Article
- Applications of Artificial Intelligence Technologies in Nursing for Noncommunicable Chronic Diseases: A Scoping Review.Journal of nursing management · 2026Article
- Stakeholder perspectives on machine learning models predicting diabetic foot ulcers and amputations in diabetes care: a scenario-based interview study.Frontiers in digital health · 2026Article
- Establishing organizational AI governance in healthcare: a case study in Canada.NPJ digital medicine · 2025Article
- Preparing hospitals and health organizations for AI: practical guidelines for the required infrastructure.Frontiers in digital health · 2025Article
- The role of artificial intelligence in enhancing breast cancer screening and diagnosis: A review of current advances.BioImpacts : BI · 2025Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial Intelligence (AI)-based tools have shown potential to optimize clinical workflows, enhance patient quality and safety, and facilitate personalized treatment. However, transitioning viable AI solutions to clinical implementation remains limited. To understand the challenges of bringing AI into clinical practice, we explored the experiences of healthcare professionals, researchers, and Policy and Governance Experts in hospitals. We conducted a qualitative study with thirteen semi-structured interviews (mean duration 52.1 ± 5.4 minutes) with healthcare professionals, researchers, and Policy and Governance Experts, with prior experience on AI development in hospitals. The interview guide was based on value, application, technology, governance, and ethics from the Innovation Funnel for Valuable AI in Healthcare, and the discussions were analyzed through thematic analysis. Six themes emerged: (1) demand-pull vs. tech-push: AI development focusing on innovative technologies may face limited success in large-scale clinical implementation. (2) Focus on generating knowledge, not solutions: Current AI initiatives often generate knowledge without a clear path for implementing AI models once proof-of-concept is achieved. (3) Lack of multidisciplinary collaboration: Successful AI initiatives require diverse stakeholder involvement, often hindered by late involvement and challenging communication. (4) Lack of appropriate skills: Stakeholders, including IT departments and healthcare professionals, often lack the required skills and knowledge for effective AI integration in clinical workflows. (5) The role of the hospital: Hospitals need a clear vision for integrating AI, including meeting preconditions in infrastructure and expertise. (6) Evolving laws and regulations: New regulations can hinder AI development due to unclear implications but also enforce standardization, emphasizing quality and safety in healthcare. In conclusion, this study highlights the complexity of AI implementation in clinical settings. Multidisciplinary collaboration is essential and requires facilitation. Balancing divergent perspectives is crucial for successful AI implementation. Hospitals need to assess their readiness for AI, develop clear strategies, standardize development processes, and foster better collaboration among stakeholders.
Identifiers
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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.