Navigating the Pandemic with AI

The purpose of this work is to present a comprehensive bibliometric analysis of the global research landscape on the application of artificial intelligence (AI) for Covid-19 using X-ray and computed tomography (CT) images, with a focus on sustainability. The study covers the publications from 2020 to March 2024 from six well-known databases (ScienceDirect, Springer, IEEE Xplore, PubMed, MDPI, and ACM). Through an exhaustive investigation of publication trends, research geographic distribution, techniques used, and publication channels, the study highlights the predominance of Deep Learning methods as employed techniques over Machine Learning and hybrid approaches. In addition, a substantial shift in the geographic distribution of publications, with emerging countries in economy and technology like India, China, Turkey, and Pakistan, is a notable finding that can be raised. The emergence of journal as the preferred platform for publishing research findings underscores the importance of peer-reviewed, detailed contributions to the body of knowledge. Furthermore, this study emphasizes the need for sustainable practices in the development and deployment of AI technologies, advocating for innovative Deep Learning models that not only enhance diagnostic accuracy but also minimize environmental impact. Furthermore, this study outlines future research directions, underscoring the necessity of innovative Deep Learning models, cross-cultural cooperation, and integration of traditional diagnostic tools with AI while prioritizing sustainability. The results provide insightful information for academics, researchers, healthcare professionals, and decision-makers seeking to use AI to enhance pandemic preparedness and response sustainably.