Artificial Intelligence and Regional Health Vulnerabilities: A Bibliometric Review of Global Trends in Non-Communicable Disease Research

Abstract

Non-communicable diseases (NCDs), including cardiovascular disease, diabetes, cancer, and chronic respiratory conditions, are the leading causes of death worldwide. These diseases are driven by socioeconomic disparities, unhealthy lifestyles, and environmental factors. AI can improve NCD vulnerability mapping through the use of big data and risk prediction, but faces data and access barriers in developing countries. This study explores how AI can be leveraged to more effectively map regional NCD vulnerability. This study seeks to analyze global research trends in the application of Artificial Intelligence (AI) to map regional vulnerability to non-communicable diseases (NCDs). This study uses a bibliometric analysis of Scopus-indexed, peer-reviewed articles (2006–2025) on AI and NCD vulnerability, processed with Excel and VOSviewer. A total of 3,626 articles were analyzed, showing a sharp rise in AI–NCD research since 2007, peaking in 2024 with 723 publications and 35,148 citations. Frequent keywords included “machine learning”, “COVID-19”, “mental health”, “diabetes”, and “artificial intelligence”. The field is marked by strong international collaboration, led by the US, China, and the UK, with over 96% of studies involving multi-country co-authorship. AI shows strong potential for mapping regional NCD vulnerability, improving early detection, and enhancing predictive risk assessment to strengthen national health policies.

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