ReviewPloS one2024
A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design.
Review in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
75 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Artificial Intelligence Platform Architecture for Hospital Systems: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Artificial intelligence in the identification and prediction of adverse transfusion reactions(ATRs) and implications for clinical management: a systematic review of models and applications.BMC medical informatics and decision making · 2025Pooled it
- Physicians' required competencies in AI-assisted clinical settings: a systematic review.British medical bulletin · 2025Pooled it
- Comparative diagnostic accuracy of artificial intelligence-derived risk stratification versus conventional risk stratification methods in pulmonary hypertension patients: a systematic review and meta-analysis.Frontiers in artificial intelligence · 2025Pooled it
- Artificial Intelligence Readiness in Emergency Medicine: Expert Consensus Opinion for Preparing the Workforce.Journal of the American College of Emergency Physicians open · 2026Article
- Digital clinical decision support to save costs: a qualitative evaluation of the views of frontline healthcare professionals.Proceedings (Baylor University. Medical Center) · 2026Article
- Clinician Trust and Human Factors in AI-Enabled Clinical Decision Support in Acute Care: Mixed Methods Study.JMIR human factors · 2026Article
- AI for Health Care Quality and Patient Safety: Scoping Review of Diagnostic, Predictive, and Decision Support Applications.Journal of medical Internet research · 2026Article
- Ethical Governance of Open Data Across Biomedical Research, Healthcare, and Public Health: Privacy, Equity, Trust, and Controlled Access.Health care analysis : HCA : journal of health philosophy and policy · 2026Article
- Beyond Traditional Risk Scores: Artificial Intelligence in Coronary Plaque Characterization and Personalized Atherosclerosis Management.Journal of cardiovascular development and disease · 2026Review
- Artificial intelligence in health systems of the WHO European region: implementation, applications, opportunities, and barriers.International journal of medical informatics · 2026Article
- When is AI "just another innovation"? A comparative conceptual analysis of artificial intelligence and evidence-based practice implementation.Implementation science communications · 2026Article
- The Cutting Edge: A Systematic Review of Artificial Intelligence and Machine Learning in Predicting Esophagectomy Outcomes.Annals of thoracic surgery short reports · 2026Article
- The global surgical workforce crisis: narrative review of the role of surgical educator development and technological innovations.Surgery today · 2026Review
- The Challenges of AI Applications in Health Care: Qualitative Study.Journal of medical Internet research · 2026Article
- Article
- A lifecycle governance and learning health system framework for trustworthy, generalizable, and sustainable human-ai partnership in clinical practice: Lessons from the asthma-guidance and prediction system (A-GPS).Journal of the National Medical Association · 2026Review
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Patients' and Providers' Attitudes Toward Artificial Intelligence and Electronic Health Record Use in Deep Phenotyping and Rare-Disease Screening: An Empty Systematic Review.Healthcare (Basel, Switzerland) · 2026Review
- Article
15 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption and allows for better anticipation and planning. This study's objective was to create a detailed inventory of barriers to and strategies for AI implementation in healthcare to support advancements in methods and implementation processes in healthcare. A sequential explanatory mixed method design was used. Firstly, scoping reviews and systematic literature reviews were identified using PubMed. Selected studies included empirical cases of AI implementation and use in clinical practice. As the reviews were deemed insufficient to fulfil the aim of the study, data collection shifted to the primary studies included in those reviews. The primary studies were screened by title and abstract, and thereafter read in full text. Then, data on barriers to and strategies for AI implementation were extracted from the included articles, thematically coded by inductive analysis, and summarized. Subsequently, a direct qualitative content analysis of 69 interviews with healthcare leaders and healthcare professionals confirmed and added results from the literature review. Thirty-eight empirical cases from the six identified scoping and literature reviews met the inclusion and exclusion criteria. Barriers to and strategies for AI implementation were grouped under three phases of implementation (planning, implementing, and sustaining the use) and were categorized into eleven concepts; Leadership, Buy-in, Change management, Engagement, Workflow, Finance and human resources, Legal, Training, Data, Evaluation and monitoring, Maintenance. Ethics emerged as a twelfth concept through qualitative analysis of the interviews. This study illustrates the inherent challenges and useful strategies in implementing AI in healthcare practice. Future research should explore various aspects of leadership, collaboration and contracts among key stakeholders, legal strategies surrounding clinicians' liability, solutions to ethical dilemmas, infrastructure for efficient integration of AI in workflows, and define decision points in the implementation process.
Indexed as
Identifiers
What OpenQuestion holds
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.