Health research policy and systems · 2025
Artificial-intelligence-driven governance: addressing emerging risks with a comprehensive risk-prevention-centred model for public health crisis management.
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Lee Ching-Hung, Wang Zhichao, Wang Dianni, Lyu Shupeng, Chen Chun-Hsien
Аннотация
BACKGROUND: In 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.
METHODS: This 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.
RESULTS: The 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.
CONCLUSIONS: The 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.
METHODS: This 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.
RESULTS: The 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.
CONCLUSIONS: The 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.