ArticleHealth care analysis : HCA : journal of health philosophy and policy2025
Analysing the Suitability of Artificial Intelligence in Healthcare and the Role of AI Governance.
Article in Health care analysis : HCA : journal of health philosophy and policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis 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
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence Governance in Health Systems: Systematic Review of Frameworks and Integrative Model Proposal.Journal of medical Internet research · 2026Pooled it
- Factors Influencing Adoption of Large Language Models in Health Care: Multicenter Cross-Sectional Mixed Methods Observational Study.Journal of medical Internet research · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
In recent years, artificial intelligence (AI) has become more important in healthcare. It has the ability to completely change how patients are diagnosed, treated, and cared for. To make sure AI is properly supervised in healthcare, many problems need to be solved. This calls for a broad approach that includes policy, technology, and involving important people. This study investigates the governance of AI within healthcare, highlighting the importance of policy, technology, and stakeholder engagement. Adopting a mixed-methods research design, the study encompasses surveys, interviews, and document analysis to comprehensively explore diverse perspectives on AI governance. Purposive sampling techniques were employed to gather 897 valid samples, ensuring diversity across stakeholder groups. Surveys gathered quantitative data on demographic characteristics and attitudes toward AI governance, while interviews provided deeper insights into stakeholders' experiences and recommendations. Document analysis supplemented data collection by reviewing policy documents, guidelines, and academic literature related to AI governance. This study merges quantitative and qualitative data to thoroughly investigate AI governance, enabling the identification of policy implications and actionable recommendations. This study contributes novel insights by adopting a comprehensive approach to AI governance in healthcare, integrating policy, technology, and stakeholder engagement perspectives. Unlike previous studies focusing solely on individual aspects of AI governance, this research provides a holistic understanding of the complex dynamics involved. This research offers important insights into AI governance by investigating the impact of stakeholder engagement, ethical considerations, digital health disparities, governance structures, and health communication strategies on AI integration in healthcare, ultimately aiding in policy development and implementation.
Indexed as
Identifiers
40048068What Socratic 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 Socratic graph.