ArticleDigital health
The evolving landscape of artificial intelligence in patient education: A bibliometric knowledge mapping study.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Trends in pediatric traumatic brain injury-related mortality in the United States from 1999 to 2024: a retrospective cross-sectional analysis.Annals of medicine and surgery (2012) · 2026Article
- The quality and reliability of short videos about cervical spondylosis on TikTok and Redbook: a cross-sectional study.BMC musculoskeletal disorders · 2026Article
- Generative AI in psychiatric education: balancing risks and benefits for responsible integration.Frontiers in psychiatry · 2026Article
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
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) is emerging as a transformative force in digital health, offering novel solutions to overcome traditional barriers in patient education, such as the low readability of materials and the high cost of personalization. The rapid integration of Large Language Models (LLMs) necessitates a clear understanding of the current research landscape to guide effective and ethical implementation. Objective: This study aims to systematically map the global research landscape of AI in patient education. This bibliometric analysis identifies the knowledge structure, research hotspots, key contributors, and evolutionary trends to guide future research and practice in this rapidly emerging domain. Methods: We retrieved 837 relevant documents published between 2016 and 2025 from the Web of Science Core Collection. Bibliometric data were analyzed using CiteSpace and RStudio to conduct visual analyses of publication trends, international collaborations, co-citation networks, and keyword evolution. Results: The analysis revealed an exponential increase in publications since 2021, a trend that strongly coincides with the advent of LLMs. The USA and China are the primary research contributors, with Harvard University leading institutional output. Research hotspots have evolved from foundational concepts like machine learning to application-focused themes such as health literacy, readability, and adherence. The intellectual base is highly interdisciplinary, drawing from medicine, computer science, and education. Conclusion: AI is rapidly transforming patient education, with a clear trajectory from technology-focused validation to patient-centered outcomes. While LLMs show immense potential, significant challenges persist regarding accuracy, ethical implementation, and systematic integration into clinical workflows. Future efforts must prioritize developing robust validation frameworks and strengthening international collaboration to enhance digital health equity.
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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.