An Academic Viewpoint (2025) on the Integration of Generative Artificial Intelligence in Medical Education:...

Area of Science:

• Medical Education Technology
• Artificial Intelligence in Healthcare
• Digital Health

Background:

• Generative artificial intelligence (GAI) presents transformative potential for medical education.
• Traditional methods face challenges in teaching, assessment, and clinical training.
• GAI offers innovative solutions to enhance learning experiences and administrative efficiency.

Purpose of the Study:

• To explore current and potential applications of GAI in medical education.
• To assess the feasibility and impact of GAI across various educational domains.
• To identify key areas where GAI can optimize learning and training.

Main Methods:

• A narrative review methodology was employed.
• A structured literature search was conducted in January 2025 across PubMed, Scopus, and Google Scholar.
• Expert analysis, including iterative brainstorming and appraisal by six domain experts, was utilized.

Main Results:

• Ten domains of GAI application were identified: Quality and Administration, Curriculum Development, Teaching and Learning, Assessment and Evaluation, Clinical Training, Academic Guidance, Student Research, Student Affairs, Internship Management, and Student Activities.
• GAI facilitates personalized learning via adaptive tutoring and performance dashboards.
• GAI enhances clinical training through immersive simulations and virtual patient encounters, and streamlines research processes.

Conclusions:

• GAI holds significant potential to enhance educational quality and operational effectiveness in medical settings.
• Ethical considerations, including data privacy, algorithmic bias, and equitable access, require robust frameworks and policies.
• Targeted and ethically guided GAI implementation can equip future healthcare professionals with essential adaptive skills for patient-centered care.