Mapping research trends and hotspots of artificial intelligence, and machine learning in medicine and health within the Eastern Mediterranean region‎: A comprehensive bibliometric analysis

Abstract

Background

Artificial Intelligence (AI) and Machine Learning (ML) literature on medicine and healthcare within the Eastern Mediterranean Region (EMR) is proliferating, and the volumes of scientific data are getting increasingly complex to analyze. It is essential to comprehensively analyze the current state of research in this area. This study performs comprehensive bibliometric analysis to evaluate the productivity and impact of publications on AI and ML in medicine and health within the EMR and to anticipate future research directions in the field.

Methods

The literature between 1996 and 2024 was retrieved from Scopus and Web of Science (WoS) databases. Microsoft Excel, the R package "bibliometrix" and VOSviewer were employed to perform bibliometric analysis and to map the landscape research, identify key themes, and explore trends.

Results

A total of 2365 eligible publications were identified, showing an average annual growth rate of 22.92 % over the past two decades. Iran (37.1 %), Saudi Arabia (25.1 %) were the most productive countries in this field. The author with the most publications was Leili Tapak. The journal with the most publications was Scientific Reports, and the most active affiliation was Tehran University of Medical Sciences. The most frequent keywords were "machine learning", "COVID-19", and "artificial intelligence".

Conclusion

AI/ML research in the EMR is expanding rapidly, driven by a few high-output countries and concentrated around clinical and diagnostic themes. The findings underscore the need for enhanced intra-regional collaboration, strategic investment in AI infrastructure, and the inclusion of underrepresented countries to ensure equitable AI development across the region.