ArticleAdvances in medical education and practice2026
Differences and Trends of Artificial Intelligence in Medical Education: A Comparative Bibliometric Analysis Between China and the International Community.
Article in Advances in medical education and practice, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- AI in pharmacy education: a comparative visualization analysis of global and Chinese research trends.Frontiers in medicine · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aims to explore the application of artificial intelligence in medical education by comparing research hotspots and evolutionary trends between China and the international community, ultimately proposing informed educational practices and policy recommendations. Methods: Literature was retrieved from the core collections of CNKI and Web of Science for the period 2014-2024, limited to article and review publications. After applying a unified Boolean search strategy and deduplication, the data were analyzed using CiteSpace 6.4.R1 to examine publication trends, collaboration networks, keyword co-occurrence/clustering/burst detection, and co-citation patterns. Results: A total of 379 Chinese and 552 English records were included. Publications surged after 2018 and peaked during 2023-2024. International hotspots centered on machine learning, deep learning, and large language models for simulation-based training and clinical reasoning; Chinese studies focused on "New Medical Sciences", VR/AR, and medical imaging. The emergence of generative artificial intelligence and multimodal large models has become a new frontier in artificial intelligence research within global medical education from 2023 to 2024. Conclusion: This study is based on a comparison of two databases to reveal the hotspots and differences in artificial intelligence and medical education research between China and the international research community. It not only compensates for the time lag of existing research, but also proposes three major trends driven by artificial intelligence in the development of medical education (generative AI, personalized learning, immersive experience). A complementary pattern exists between technology-driven and scenario-driven orientations. We recommend integrating AI literacy and ethics into curricula, establishing Generative-AI teaching/assessment guidelines, and building cross-institutional, yearly knowledge-map monitoring for sustainable innovation in medical education.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.