Evidence map›Paper›PMID 42254636›Full record

ArticleFrontiers in public health2026

Why do I use generative artificial intelligence (GenAI) to seek health information? A perceptual perspective of GenAI users.

Hui Zhu, Jianfei Ding

Abstract read
In one paragraph

Article in Frontiers in public health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Hui ZhuSchool of Marxism, Anhui Medical University, Hefei, China.
Jianfei DingSchool of Marxism, Anhui Medical University, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: China's vigorous advancement of the " Methods: This study garnered primary data via a structured online survey and employed partial least squares structural equation modeling (PLS-SEM) to dissect the antecedent factors and underlying mechanisms governing users' health information seeking intention via GenAI, grounded in the perspective of user perceptions. Results: PLS-SEM results indicate that user perceptions of GenAI (perceived competence, perceived convenience, and perceived anthropomorphism) positively influence on both user trust in GenAI and subjective norms, which in turn positively affect users 'health information seeking intention through GenAI. Moreover, digital health literacy significantly moderates the relationship between user perceptions of GenAI and their health information seeking intention. Discussion: These findings yield valuable empirical insights for facilitating the optimized and scaled adoption of GenAI in health information services, enhancing the public health output of digital health policies, improving residents' health welfare, and further alleviating the operational burdens borne by traditional healthcare resources.

Indexed as

Consumer Health InformationGenerative Artificial IntelligenceInformation Seeking BehaviorAdultChinaFemaleHumansInternetMaleMiddle AgedSurveys and QuestionnairesGenAI usersgenerative artificial intelligencehealth information seeking intentioninternet plus healthcareperceived characteristics

Identifiers

PMID42254636
PMCPMC13236855

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.