ArticleJournal of medical Internet research2025
Health Communication on the Internet: Promoting Public Health and Exploring Disparities in the Generative AI Era.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Artificial Intelligence as a Core Public Health Competency: Proposal and Perspectives in Education and Research.JMIR medical education · 2026Article
- Development of an Equity-Centered Sociotechnical Architecture for Generative AI Integration in Public Health Promotion: Conceptual Framework.Journal of medical Internet research · 2026Article
- Integrating AI-Powered Chatbots Into Patient Education From the Perspectives of Patients, Caregivers, and Nurses: Qualitative Study.Journal of medical Internet research · 2026Article
- Understanding Online Health Information Consumption Through Web Analytics of the Italian Society of Pharmacology Magazine: 3-Year Descriptive Analysis.JMIR formative research · 2026Article
- Artificial Intelligence For 6P Medicine: Consolidating AI Needs of Predictive, Preventive, Personalized, Participatory, Precision, and Public Health Trajectories.Journal of medical systems · 2026Review
- Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency.Vox sanguinis · 2026Review
- Evaluating the Use of Generative AI Videos for Health Self-Management of Older Adults: Mixed Methods Study.JMIR aging · 2026Article
- Why do I use generative artificial intelligence (GenAI) to seek health information? A perceptual perspective of GenAI users.Frontiers in public health · 2026Article
- The Health Communications Assessment Checklist: a teaching tool for uncovering bias in the age of AI.Frontiers in public health · 2026Article
- Can ChatGPT-5 educate the public about vasectomy?: a Google Trends-based expert panel assessment.Frontiers in digital health · 2026Article
- Quality, reliability, and transparency of late-life depression videos on Chinese social media: a cross-sectional study of Douyin, Rednotes, and BiliBili.Frontiers in psychiatry · 2026Article
- Preparedness for generative AI adoption among Chinese cancer survivors: a multi-center cross-sectional survey study.Frontiers in public health · 2026Article
- The role of personality traits in predicting educational use of generative AI in higher education.Scientific reports · 2025Article
- Co-Design of a Health Screening Program Fact Sheet by People Experiencing Homelessness and ChatGPT: Focus Group Study.JMIR formative research · 2025Article
- Quality and reliability of pediatric pneumonia related short videos on mainstream platforms: cross-sectional study.BMC public health · 2025Article
- Effective Communication Training Needs Among US State and Local Public Health Professionals: Findings From the 2024 Public Health Workforce Interest and Needs Survey.Journal of public health management and practice : JPHMPArticle
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
Health communication and promotion on the internet have evolved over time, driven by the development of new technologies, including generative artificial intelligence (GenAI). These technological tools offer new opportunities for both the public and professionals. However, these advancements also pose risks of exacerbating health disparities. Limited research has focused on combining these health communication mediums, particularly those enabled by new technologies like GenAI, and their applications for health promotion and health disparities. Therefore, this viewpoint, adopting a conceptual approach, provides an updated overview of health communication mediums and their role in understanding health promotion and disparities in the GenAI era. Additionally, health promotion and health disparities associated with GenAI are briefly discussed through the lens of the Technology Acceptance Model 2, the uses and gratifications theory, and the knowledge gap hypothesis. This viewpoint discusses the limitations and barriers of previous internet-based communication mediums regarding real-time responses, personalized advice, and follow-up inquiries, highlighting the potential of new technology for public health promotion. It also discusses the health disparities caused by the limitations of GenAI, such as individuals' inability to evaluate information, restricted access to services, and the lack of skill development. Overall, this study lays the groundwork for future research on how GenAI could be leveraged for public health promotion and how its challenges and barriers may exacerbate health inequities. It underscores the need for more empirical studies, as well as the importance of enhancing digital literacy and increasing access to technology for socially disadvantaged populations.
Indexed as
Identifiers
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.