REVIEW article
Front. Digit. Health
Sec. Health Technology Implementation
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1621026
Post-COVID-19 Digital Health Resilience: Comparative Strategies of AI, Big Data, and Governance in Four Countries
Provisionally accepted
- King Abdulaziz University, Jeddah, Saudi Arabia
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Background The COVID-19 global pandemic marked a pivotal moment in the public adoption of Artificial Intelligence (AI) and Big Data technologies, leading to increased utilization for public health surveillance, forecasting, and decision-making. While countries were quick to implement digital interventions, there is little cross-national evidence that shows how these interventions necessarily revealed enabling frameworks and recurring barriers. Purpose The purpose of this review is to evaluate how selected countries: India, the United States, Taiwan, and South Korea adopted AI and big-data technologies and practices during and after their COVID-19 experience, and to find transferable lessons and continuing best practices to inform the building of robust digital health governance and pandemic preparedness systems. Design/Methodology The review is based on the thirty (30) studies to assess digital health intervention strategies used in the four countries. Explorations uncovered themes such as data interoperability, public trust, legal and regulatory readiness, and the application of AI to routine health governance. Findings Taiwan and South Korea proved to be highly effective through centralized data infrastructures, legally framed constitutions, and high levels of public compliance. India and the US, although having large technology capabilities, demonstrated limited practices because of incomplete governance and unequal access. The cross-country synthesis underscores the importance of institutional memory, transparent digital policies, public-private partnerships, and inclusive digital ecosystems. This is a provisional file, not the final typeset article Limitations/Future Research This study is based on secondary data and literature review; it does not include primary fieldwork or stakeholder interviews. Future research should assess the longitudinal outcomes of AI-based public health strategies and their adaptability in diverse health system settings.
Keywords: artificial intelligence, big data, COVID-19, Digital Health, strategies
Received: 02 May 2025; Accepted: 30 Sep 2025.
Copyright: © 2025 Alwaf, Alrougi, Alsulami, Badri, Alahmadi and Fakieh. 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: Sahar Badri, skbadri@kau.edu.sa
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.