ABSTRACT:
Health inequality remains a significant global challenge, particularly in border regions and developing nations. As technology advances, Artificial Intelligence (AI) has emerged as a potential instrument to bridge these gaps. However, a comprehensive global research roadmap integrating AI with health equity remains insufficiently mapped. This study aims to analyze global literature trends regarding the application of AI in supporting health equity over the past decade. The methodology employs a quantitative bibliometric analysis with data extracted from the Scopus and Web of Science databases. The analysis is performed using Bibliometrix (R), VOSviewer, and CiteSpace to visualize co-citation networks, keyword evolution, and geographical collaborations. This study evaluates over a thousand scholarly articles to identify primary research clusters, dominant contributing countries, and existing research gaps. The results reveal a significant shift in focus from purely algorithmic accuracy toward inclusive and ethical AI implementation. Findings indicate that while publications have surged, collaboration between technology developers in high-income countries and public health practitioners in low-resource settings remains limited. Network visualization highlights that topics such as "Algorithmic Bias" and the "Digital Divide" are crucial trends determining the future success of health technologies. In conclusion, this research presents a strategic framework for policymakers and engineers to prioritize sustainable and inclusive AI development. This study proves that technology without equity-oriented policies only serves to widen the global health divide. Through this mapping, it is hoped that future research can be more precisely focused on technological solutions that are truly "without borders."