Applications, Challenges, and Prospects of Generative Artificial Intelligence Empowering Medical Education: Scoping...

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Educational Innovation

Background:

• Generative artificial intelligence (GAI) is significantly advancing medical education through enhanced intelligence, personalization, and interactivity.
• GAI redefines access to educational resources, teaching methodologies, and assessment strategies in the medical field.

Purpose of the Study:

• To review current Generative artificial intelligence (GAI) applications in medical education.
• To analyze the opportunities, challenges, strengths, and potential issues of GAI in educational methods, assessments, and resources.
• To capture the rapid evolution and multidimensional applications of GAI in medical education for future practice.

Main Methods:

• A scoping review of literature from January 2023 to October 2024 using PubMed, Web of Science, and Scopus.
• Adherence to PRISMA-ScR guidelines, involving a two-stage screening process of 5991 retrieved articles.
• Integration of quantitative analysis of publication trends and Human Development Index (HDI) with thematic analysis of applications, limitations, and ethical implications.

Main Results:

• Most studies (74.0%) originated from very high HDI countries, with the United States leading in contributions.
• ChatGPT was the most frequently studied GAI model (n=119), followed by Gemini (n=22).
• Key applications include diversifying educational methods, improving assessment evaluation, and optimizing resources, alongside identified challenges like adaptability, data bias, and ethical concerns.

Conclusions:

• Significant regional disparities exist in GAI development for medical education, with specific model preferences observed.
• GAI offers potential for medical education empowerment, but technical and ethical hurdles must be addressed for widespread adoption.
• Advocacy for a tripartite resource-method-assessment model, specialized GAI development, and an ecosystem promoting human-machine symbiosis for efficient, human-centered medical education.