Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China’s success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

This study examines the multifaceted impact of artificial intelligence (AI) on environmental sustainability, specifically targeting ecological footprints, carbon emissions, and energy transitions. Utilizing panel data from 67 countries, we employ System Generalized Method of Moments (SYS-GMM) and Dynamic Panel Threshold Models (DPTM) to analyze the complex interactions between AI development and key environmental metrics. The estimated coefficients of the benchmark model show that AI significantly reduces ecological footprints and carbon emissions while promoting energy transitions, with the most substantial impact observed in energy transitions, followed by ecological footprint reduction and carbon emissions reduction. Nonlinear analysis indicates several key insights: (i) a higher proportion of the industrial sector diminishes the inhibitory effect of AI on ecological footprints and carbon emissions but enhances its positive impact on energy transitions; (ii) increased trade openness significantly amplifies AI’s ability to reduce carbon emissions and promote energy transitions; (iii) the environmental benefits of AI are more pronounced at higher levels of AI development, enhancing its ability to reduce ecological footprints and carbon emissions and promote energy transitions; (iv) as the energy transition process deepens, AI’s effectiveness in reducing ecological footprints and carbon emissions increases, while its role in promoting further energy transitions decreases. This study enriches the existing literature by providing a nuanced understanding of AI’s environmental impact and offers a robust scientific foundation for global policymakers to develop sustainable AI management frameworks.

This paper investigates the intricate relationship between artificial intelligence (AI) and green innovation within the context of sustainable development goals. As societies strive to achieve sustainability, understanding the dynamics between technological advancements and environmental progress becomes paramount. Drawing from panel data encompassing 51 countries between 2000 and 2019, this study employs fixed-effects models, mediated effects models, and spatial Durbin models to meticulously examine the influence of AI on green innovation. The empirical findings reveal a robust and significantly positive correlation between AI and green innovation, highlighting the critical role of AI in fostering environmental innovation. Heterogeneity analysis across developed and developing economies delineates variations in the impact of AI on green innovation, shedding light on the influence of economic development levels and financial structures. Developed nations showcase a more pronounced AI-green innovation relationship compared to their developing counterparts, highlighting the complexities of technology adoption within distinct economic landscapes. Moreover, this study delves into the transmission mechanisms underlying the AI-green innovation nexus, revealing the mediating roles of industrial structure and human capital. Industrial upgrading and the enhancement of human capital emerge as crucial pathways through which AI indirectly stimulates green innovation. Spatial analyses reveals the spatial relevance of green innovation globally, emphasizing AI's substantial impact not only within domestic spheres but also across neighboring regions. There are significant direct, indirect, and total effects of AI on green innovation, highlighting its spillover characteristics and the catalytic role it plays in driving collaborative AI development on a global scale. This research contributes nuanced insights into the interplay between AI and green innovation, providing a foundation for policymakers, businesses, and researchers to comprehend the multifaceted dimensions of technological interventions in fostering sustainable innovation. The findings emphasize the imperative of collaborative efforts in utilizing AI's potential to propel green innovation, thereby advancing global sustainability agendas.