Evidence map›Paper›PMID 40053755›Full record

ArticleJournal of medical Internet research2025

Health Communication on the Internet: Promoting Public Health and Exploring Disparities in the Generative AI Era.

Jamal Uddin, Cheng Feng, Junfang Xu

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Jamal UddinCommunication Department, Cornell University, Ithaca, NY, United States.ORCID https://orcid.org/0000-0002-2612-6168
Cheng FengVanke School of Public Health, Tsinghua University, Beijing, China.ORCID https://orcid.org/0000-0002-5599-460X
Junfang XuDepartment of Pharmacy, Second Affiliated Hospital, School of Public health, Zhejiang University School of Medicine, Hangzhou, China.ORCID https://orcid.org/0000-0001-9133-8960

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Artificial IntelligenceHealth CommunicationHealth PromotionInternetPublic HealthHumansAIartificial intelligenceChatGPTcommunicationdisparitygenAIgenerativegenerative AIgratificationgratification theoryhealthhealth communicationhealth disparityhealth promotioninequityinternetpublic healthtool

Identifiers

PMID40053755
PMCPMC11926442

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.