Twelve tips for developing and implementing AI curriculum for undergraduate medical education

Area of Science:

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

Background:

• The increasing integration of artificial intelligence (AI) into clinical practice necessitates AI education for medical professionals.
• Current undergraduate medical curricula require adaptation to incorporate AI's unique aspects and applications.
• A structured approach is needed to develop and implement AI curricula effectively.

Purpose of the Study:

• To provide actionable guidance for developing and implementing AI curricula in undergraduate medical education.
• To outline key considerations for aligning AI education with existing medical frameworks and digital health initiatives.
• To ensure medical graduates are prepared for AI-augmented medical practice.

Main Methods:

• Development of twelve actionable tips for AI curriculum design and implementation.
• Emphasis on defining the purpose, scope, and integration of AI education.
• Focus on progressive learning, resource allocation, authentic experiences, and continuous evaluation.

Main Results:

• A framework with twelve practical recommendations for creating robust AI curricula.
• Strategies for aligning AI education with competency frameworks and digital health.
• Methods for ensuring sustainability through resources, experiential learning, and evaluation.

Conclusions:

• Effective AI curricula are essential for preparing medical students for AI in healthcare.
• A systematic approach to curriculum development, resource management, and evaluation is vital.
• Medical schools must adapt to integrate AI education to foster future-ready physicians.