Evidence map›Paper›PMID 41013547›Full record

ArticleHealth research policy and systems2025

Artificial-intelligence-driven governance: addressing emerging risks with a comprehensive risk-prevention-centred model for public health crisis management.

Ching-Hung Lee, Zhichao Wang, Dianni Wang, Shupeng Lyu, Chun-Hsien Chen

Abstract read
In one paragraph

Article in Health research policy and systems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Ching-Hung Lee *School of Public Policy and Administration, Xi'an Jiaotong University1, Xi'an, China.
Zhichao Wang *School of Public Policy and Administration, Xi'an Jiaotong University1, Xi'an, China.
Dianni WangSchool of Public Policy and Administration, Xi'an Jiaotong University1, Xi'an, China.
Shupeng LyuSchool of Public Policy and Administration, Xi'an Jiaotong University1, Xi'an, China. shupenglv@126.com.
Chun-Hsien ChenSchool of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundIn response to the coronavirus disease 2019 (COVID-19) pandemic, an emerging public health crisis with global impact, various artificial intelligence (AI)-enabled devices for pandemic-prevention emerged, highlighting the urgent need to understand public leverage of AI-enabled digital technologies.

methodsThis study constructs a comprehensive model, the Risk Prevention-centred and AI-enabled Anti-pandemic Technology Acceptance Model (RPAA-TAM), to elucidate public adoption of anti-pandemic digital tools, contributing to innovative governance. Integrating TAM, social influence theory and risk perception theory, RPAA-TAM analyses technology development and explores factors influencing public acceptance of AI in pandemic prevention.

resultsThe study identifies seven key factors impacting public acceptance, including external variables, public trust, perceived benefit, perceived risk, attitude toward use, behavioural intention to use and system usage, offering insights into the integration of AI in managing emerging public health crises. The study offers seven novel propositions derived from a literature review on the basis of the RPAA-TAM.

conclusionsThe Risk Prevention-centred and AI-enabled Anti-pandemic Technology Acceptance Model (RPAA-TAM) offers a comprehensive framework for understanding public acceptance of AI in pandemic prevention. Identifying seven key factors impacting acceptance, our study provides novel propositions on the basis of literature review. RPAA-TAM contributes to innovative governance strategies, guiding the ethical and socially acceptable integration of AI in managing public health crises.

Indexed as

Artificial IntelligenceCOVID-19PandemicsPublic HealthHumansSARS-CoV-2TrustArtificial intelligence (AI)COVID-19 pandemicEmerging public health crisisEvolutionary technology acceptance model

Identifiers

PMID41013547
PMCPMC12465421

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.