SynthesisFrontiers in digital health2026
Stakeholder experience with artificial intelligence in healthcare: a bibliometric study of satisfaction, trust, acceptance, and patient engagement.
Synthesis in Frontiers in digital health, 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The implementation of artificial intelligence (AI) in healthcare increasingly depends not only on algorithmic performance but also on how patients and healthcare professionals experience, trust, accept, and engage with AI-enabled systems. Although many bibliometric studies have mapped AI in healthcare, the human-centered evidence base on satisfaction-related stakeholder experience remains fragmented. This study therefore analyzed satisfaction as one component of a broader stakeholder-experience framework that also includes trust, attitude, perception, acceptance, willingness, resistance, and usability. Methods: English-language articles, reviews, and conference papers published between January 1, 2010 and December 31, 2025 were retrieved from the Web of Science Core Collection and Scopus. After duplicate removal and eligibility screening, 1,794 publications were included. Bibliometric analyses were conducted using Microsoft Excel, VOSviewer, CiteSpace, and bibliometrix. Results: Publication output increased rapidly after 2020 and reached 696 publications in 2025. The United States and China were the leading contributors, and the Journal of Medical Internet Research was the most productive journal. Keyword and co-citation analyses showed a shift from early work on robotic surgery and clinical decision support toward human-centered topics, including patient satisfaction, trust, attitude, ChatGPT, large language models, explainable AI, ethics, and nursing workforce adaptation. Three interconnected frontiers were identified: large-language-model-mediated clinical communication, explainability and trust in patient-AI-professional relationships, and the educational and professional needs of nurses and other healthcare workers. Conclusion: This study maps the knowledge structure and thematic evolution of research on stakeholder experience with healthcare AI. The findings show that satisfaction should not be treated as interchangeable with trust, acceptance, or usability; rather, these constructs jointly define a multidimensional human-centered implementation problem. Future research should integrate explainable AI, ethical governance, regulatory compliance, and active patient engagement into the evaluation of healthcare AI.
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What 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.