What Are the Mechanisms That Support Healthcare Professionals to Adopt Artificial Intelligence Into Practice?
Bibliographic record
Abstract
Artificial intelligence (AI) is increasingly integrated across healthcare disciplines, promising enhanced accuracy, efficiency, and personalized care. However, clinical adoption remains inconsistent due to organizational, ethical, and behavioral barriers. Earlier evidence syntheses predate recent developments in generative AI, global policy regulation, and post-pandemic digital transformation.This updated Rapid Realist Review (RRR) sought to identify and explain the mechanisms that support healthcare professionals in adopting AI technologies into clinical practice, using contemporary evidence published between January 2021 and June 2025.A realist synthesis approach was used to uncover Context–Mechanism–Outcome (CMO) patterns explaining how adoption occurs across clinical and organizational contexts. Six databases (PubMed, Scopus, Web of Science, IEEE Xplore, ScienceDirect, and Frontiers) were searched using predefined Boolean terms. Sixteen eligible studies, spanning empirical research, implementation frameworks, and conceptual analyses, were included following quality appraisal using the Mixed Methods Appraisal Tool (MMAT 2022). Data were extracted into a Mechanism × Study Evidence Matrix and synthesized iteratively through thematic abstraction, supported by expert stakeholder consultation.Seven interdependent mechanisms were identified:1.Organizational readiness and leadership (strategic alignment, governance maturity)2.Trust, transparency, and explainability (clinician confidence and interpretability),3.Workflow compatibility and integration (EHR interoperability and task alignment),4.Ethical, legal, and governance clarity (fairness, privacy, accountability),5.Training, education, and AI literacy (competence and professional readiness),6.Digital ecosystem and infrastructure readiness (interoperability and data security), and7.Behavioral and cultural adaptation (identity, autonomy, and professional norms).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.123 | 0.326 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.013 | 0.008 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".