Evidence map›Paper›PMID 40414992›Full record

ArticleScientific reports2025

Investigating the factors influencing users' adoption of artificial intelligence health assistants based on an extended UTAUT model.

Jiayu Su, Yuhui Wang, Hongyi Liu, Zuopeng Zhang, Zhe Wang, Zhirong Li

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.

0numbers the graph read from it
0cells of the map it votes in
22citing 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

22 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

6 authors.

Jiayu SuSchool of Architecture and Art, Central South University, Changsha, China.
Yuhui WangSchool of Architecture and Art, Central South University, Changsha, China.
Hongyi LiuLaw School, University of Southampton, Southampton, UK.
Zuopeng ZhangSchool of Architecture and Art, Central South University, Changsha, China. 221301005@csu.edu.cn.
Zhe WangSchool of Architecture and Art, Central South University, Changsha, China.
Zhirong LiSchool of Architecture and Art, Central South University, Changsha, China. 139140@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

As an emerging healthcare technology, artificial intelligence (AI) health assistants have garnered significant attention. However, the acceptance and intention of ordinary users to adopt AI health assistants require further exploration. This study aims to identify factors influencing users' intentions to use AI health assistants and enhance understanding of the acceptance mechanisms for this technology. Based on the unified theory of acceptance and use of technology (UTAUT), we expanded the variables of perceived trust (PT) and perceived risk (PR). We recruited 373 Chinese ordinary users online and analyzed the data using covariance-based structural equation modeling (CB-SEM). The results indicate that the original UTAUT structure is robust, performance expectancy (PE), effort expectancy (EE), and social influence (SI) significantly positively affect behavioral intention (BI), while facilitating conditions (FC) do not show a significant impact. Additionally, perceived trust is closely related to performance expectancy, effort expectancy, and behavioral intention, negatively impacting perceived risk. Conversely, perceived risk adversely affects behavioral intention. Our findings provide valuable practical insights for developers and operators of AI health assistants.

Indexed as

Artificial IntelligenceAdultFemaleHumansIntentionMaleMiddle AgedSurveys and QuestionnairesTrustYoung AdultArtificial intelligence (AI)Digital healthPerceived riskPerceived trustUnified theory of technology acceptance and use (UTAUT)Usage behavior

Identifiers

PMID40414992
PMCPMC12104380

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Registered trials

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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.