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

IF 3.2 2区 医学 Q1 HEALTH POLICY & SERVICES
Ching-Hung Lee, Zhichao Wang, Dianni Wang, Shupeng Lyu, Chun-Hsien Chen
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引用次数: 0

Abstract

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.

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人工智能驱动的治理:利用以风险预防为中心的公共卫生危机管理综合模式应对新出现的风险。
背景:2019冠状病毒病(COVID-19)大流行是一场新兴的、具有全球影响的公共卫生危机,为应对这一危机,各种支持人工智能(AI)的大流行预防设备应运而生,凸显了了解人工智能支持的数字技术在公众中的作用的迫切需要。方法:本研究构建了一个综合模型,即以风险预防为中心和人工智能支持的抗流行病技术接受模型(RPAA-TAM),以阐明公众对抗流行病数字工具的采用,有助于创新治理。RPAA-TAM结合TAM、社会影响理论和风险感知理论,分析技术发展,探讨影响公众对人工智能在流行病预防中的接受程度的因素。结果:该研究确定了影响公众接受度的七个关键因素,包括外部变量、公众信任、感知利益、感知风险、使用态度、使用行为意图和系统使用情况,为将人工智能整合到管理新出现的公共卫生危机中提供了见解。本研究在RPAA-TAM的基础上,从文献综述中提出了七个新的命题。结论:以风险预防为中心、支持人工智能的抗流行病技术接受模型(RPAA-TAM)为了解公众对人工智能在流行病预防中的接受程度提供了一个全面的框架。本研究在文献回顾的基础上,提出了影响接受度的七个关键因素。RPAA-TAM有助于创新治理战略,指导人工智能在道德和社会上可接受的管理公共卫生危机方面的整合。
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来源期刊
Health Research Policy and Systems
Health Research Policy and Systems HEALTH POLICY & SERVICES-
CiteScore
7.50
自引率
7.50%
发文量
124
审稿时长
27 weeks
期刊介绍: Health Research Policy and Systems is an Open Access, peer-reviewed, online journal that aims to provide a platform for the global research community to share their views, findings, insights and successes. Health Research Policy and Systems considers manuscripts that investigate the role of evidence-based health policy and health research systems in ensuring the efficient utilization and application of knowledge to improve health and health equity, especially in developing countries. Research is the foundation for improvements in public health. The problem is that people involved in different areas of research, together with managers and administrators in charge of research entities, do not communicate sufficiently with each other.
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