JMIR Publications

ABSTRACT

Background:

Artificial intelligence (AI) is increasingly being integrated into medical education. As AI technologies continue to evolve, they are expected to enable more sophisticated student tutoring, performance evaluation, and curriculum reformation. However, medical education entities have been ill-prepared to embrace this technological revolution, and there is anxiety concerning its potential harm to the community.

Objective:

To explore research trends in the field and identify future directions for AI-enabled medical education, a systematic bibliometric approach was used.

Methods:

Documents were collected from Web of Science and Scopus databases, from the year 2000 to 2024, using a PRISMA-based protocol. VOSviewer, CiteSpace, and the R package bibliometrix were utilized for the bibliometric analysis. Co-occurrence network, keyword citation burst, and co-citation classification methods were used for downstream analysis.

Results:

After analyzing 650 documents, we found a significant growth in publications since 2020, led by the United States, with increasing non-Western participation. By delineating the development trajectory in the field, we showed that medical education has undergone a paradigm shift, transforming computer-assisted tools from solitary learning aids into interactive, social learning platforms. Our analysis revealed three key focus areas in this field: whether AI can realistically be used in training doctors, what changes schools should make to include AI, and how students react to these new AI-based teaching methods.

Conclusions:

Involving medical educators and students in AI development strengthens the medical education community's understanding and subsequent technology adoption. At the same time, we need to update our teaching programs - moving beyond rote memorization to focus on developing crucial modern skills like computational thinking and understanding how AI works.