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ORIGINAL RESEARCH article

Front. Public Health
Sec. Digital Public Health
Volume 12 - 2024 | doi: 10.3389/fpubh.2024.1506930
This article is part of the Research Topic Advancing Public Health through Generative Artificial Intelligence: A Focus on Digital Well-Being and the Economy of Attention View all articles

Research on the Impact of Artificial Intelligence Technology on Urban Public Health Resilience

Provisionally accepted
Erdong Chen Erdong Chen *Huaxin Zhang Huaxin Zhang
  • Liaoning University, Shenyang, China

The final, formatted version of the article will be published soon.

    Achieving urban public health resilience is a complex, systemic transformation that requires coordinated efforts from all sectors of society. As a key driver of high-quality development, artificial intelligence (AI) technology is poised to play a pivotal role in advancing this goal. This study constructs a comprehensive index of urban public health resilience using the entropy weight method, with AI patent data serving as the primary explanatory variable. By analyzing panel data from 284 prefecture-level cities in China, covering the years 2011 to 2021, the study provides both theoretical insights and empirical evidence on the impact of AI technology on urban public health resilience. The findings indicate that AI technology significantly enhances resilience, with more pronounced effects in eastern and central regions. In addition, the study reveals isomorphic patterns among invention patents, utility model patents, and design patents related to AI technology, highlighting that resource misallocation has prevented western cities from fully benefiting from technological advancements. Spatial analysis further demonstrates that AI technology generates positive spillover effects on public health resilience, with indirect effects from patent authorizations being particularly significant.

    Keywords: artificial intelligence, Invention patents, Utility Model Patents, design patents, Urban Public Health Resilience, Spatial effects

    Received: 06 Oct 2024; Accepted: 18 Dec 2024.

    Copyright: © 2024 Chen and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Erdong Chen, Liaoning University, Shenyang, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.