# Artificial Intelligence and Medical Education (2013–2024): A Scopus-Based Bibliometric Analysis

## TL;DR

This paper analyzes global research trends in AI's role in medical education from 2013 to 2024, highlighting growth, key contributors, and emerging themes.

## Contribution

The study provides a comprehensive bibliometric analysis of AI in medical education, focusing on global collaboration and thematic developments.

## Key findings

- Research output on AI in medical education peaked in 2024 with 1,081 publications.
- The United States leads in publication volume, followed by Russia and Canada.
- Key themes include clinical competence, virtual simulation, and ethical considerations.

## Abstract

Artificial Intelligence (AI) is transforming medical education by enabling personalised learning, adaptive feedback, simulation-based training, and automated assessments. While AI offers significant benefits, including curriculum optimisation and virtual tutoring, concerns around data privacy, access, and ethical implementation persist. Although bibliometric studies have explored AI in healthcare, comprehensive analyses of global collaboration and publication trends in AI-focused medical education remain limited.

This study aims to analyse global research trends, key contributors, and thematic developments in the application of AI within medical education.

A bibliometric analysis was conducted using the Scopus database. The search strategy included terms such as “Medical Education”, “Artificial Intelligence”, “Machine Learning”, “Deep Learning”, “Clinical Training”, “Virtual Patients”, and “Simulation”. Data were analysed using the Bibliometrix R package to assess publication volume, keyword co-occurrence, author collaboration, and citation patterns.

Research output on AI in medical education has grown significantly, peaking in 2024 with 1,081 publications. The United States leads in publication volume, followed by Russia and Canada. “Artificial Intelligence” was the most frequently used keyword. Co-authorship and co-citation networks revealed strong international collaboration, with emerging themes in clinical competence, virtual simulation, and ethical considerations.

The future of artificial intelligence in medical education is promising, with applications in personalised treatment plans, drug development, and virtual healthcare assistants. AI has transformative potential for medical education, particularly in personalised learning and simulation-based training. Strategic investment in AI literacy, ethical frameworks, and infrastructure is essential to ensure equitable and effective integration across global contexts.