ReviewHealthcare (Basel, Switzerland)2026
From Algorithm to Policy: A Bibliometric Analysis of Implementation Science and Governance Frameworks in AI Healthcare Research (2016-2026).
Review in Healthcare (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a bibliometric analysis of AI healthcare research related to implementation science and governance frameworks from 2016 to 2026. A systematic search of the Scopus database identified 3780 peer-reviewed articles published across 1500 sources. The dataset was analyzed using Biblioshiny. The findings show an annual growth rate of 47.65% in governance-focused publications, exceeding the growth rate of technical AI research. Four main research themes were identified: regulatory compliance, ethical frameworks with limited operational measures, organizational readiness, and clinical workflow integration. The United States, China, and the United Kingdom are the leading contributors, while the Journal of Medical Internet Research and BMJ Open are among the main publication outlets. International collaboration (34.66%) remains concentrated among high-income countries. Thematic development has progressed from general ethical discussions to pandemic-related applications and more specific regulatory frameworks. Three research gaps contribute to the algorithm-to-policy translation deficit: the principles-practice gap, the regulatory-evidence gap, and the innovation-implementation gap. This study proposes an integrated governance framework based on five evidence-based principles. The framework is a conceptual model derived from bibliometric findings and requires further empirical validation in clinical settings before practical adoption. The study contributes by providing a bibliometric analysis of AI governance research and introducing the concept of the "algorithm-to-policy translation deficit" as an analytical framework. It also offers a structure to support future research and practice toward safe, effective, and equitable clinical implementation of AI. These findings guidance for regulators, healthcare organizations, AI developers, and researchers.
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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.