Embedding Artificial Intelligence Competencies in Military Medical Education: A Longitudinal Framework

Area of Science:

• Medical Education
• Artificial Intelligence in Healthcare
• Military Medicine

Background:

• Artificial intelligence (AI) is increasingly integrated into Military Health System (MHS) operations, impacting diagnosis, planning, and health protection.
• Current undergraduate medical education (UME) lacks comprehensive AI training, leading to a readiness gap for future military medical officers.
• Existing AI education is fragmented, hindering the development of skills needed to manage AI tools, recognize bias, and apply them in austere environments.

Purpose of the Study:

• To propose a longitudinal, competency-based framework for integrating AI education into UME.
• To embed AI competencies within existing medical education domains, rather than as a standalone subject.
• To enhance the readiness of future military medical officers for AI-driven healthcare in operational settings.

Main Methods:

• Developed a framework aligned with the 2024 Foundational Competencies for Undergraduate Medical Education.
• Proposed a scaffolded progression across pre-clerkship, clerkship, and post-clerkship phases.
• Emphasized integration within existing domains like Patient Care and Systems-Based Practice.

Main Results:

• The framework facilitates foundational knowledge acquisition in pre-clerkship.
• Enables application of AI tools in clinical documentation and decision-making during clerkship.
• Refines ethical judgment, privacy awareness, and operational readiness for graduate medical education.

Conclusions:

• Integrating AI education across UME is crucial for force readiness and mission success.
• Competency-based AI training ensures military medical officers can evaluate AI, operate in austere settings, and communicate effectively.
• Successful implementation requires curriculum design, faculty development, IT governance, and alignment with military training pipelines.