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

Artificial intelligence (AI) is rapidly transforming healthcare delivery, logistics, and operational decision-making across the Military Health System (MHS). Artificial intelligence-enabled tools now influence diagnosis, documentation, casualty prediction, mission planning, evacuation prioritization, and force health protection in both garrison and deployed environments. Despite this accelerating change, AI education in undergraduate medical education (UME) remains fragmented and largely confined to electives or isolated workshops, creating a readiness gap for future military medical officers. Current approaches do not provide students with the skills necessary to interpret AI-enabled decision-support tools, recognize algorithmic bias, or apply these technologies in austere, bandwidth-limited settings where command authority and operational priorities may shape clinical judgment. This commentary proposes a longitudinal, competency-based framework for integrating AI education across the pre-clerkship, clerkship, and post-clerkship phases of UME. Grounded in the updated 2024 Foundational Competencies for Undergraduate Medical Education, the framework embeds AI-related sub-competencies within existing domains-Patient Care, Systems-Based Practice, Interpersonal and Communication Skills, Professionalism, and others-rather than establishing AI as a standalone topic. A scaffolded progression rooted in Competency Based Medical Education enables students to build foundational knowledge during pre-clerkship, apply AI tools in clinical documentation and decision-making during clerkship, and refine ethical judgment, privacy awareness, and operational readiness as they prepare to transition to graduate medical education. Successful implementation will require intentional curriculum design, faculty development, IT governance, and alignment with existing military training pipelines, including operational medicine field exercises. Embedding AI across the curriculum has direct implications for force readiness. Military medical officers who can evaluate AI outputs, integrate algorithmic tools in austere environments, and communicate uncertainty to patients and line leadership will be better prepared to support casualty care, mission planning, and operational decision-making in large-scale combat operations. Early assessed and sustained competency ensures both patient safety and mission success in future landscapes of war.

Authors

Institutions

Publication Details

Journal
Military Medicine
Published
2026-02-04
DOI
https://doi.org/10.1093/milmed/usag068
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Cole Jt, Justin G. Peacock, Rebekah Cole, Anita Samuel et al.
Military Medicine
Artificial Intelligence in Healthcare and Education
article

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

Cole Jt, Justin G. Peacock, Rebekah Cole, Anita Samuel, Joshua Duncan
article en

Abstract

Artificial intelligence (AI) is rapidly transforming healthcare delivery, logistics, and operational decision-making across the Military Health System (MHS). Artificial intelligence-enabled tools now influence diagnosis, documentation, casualty prediction, mission planning, evacuation prioritization, and force health protection in both garrison and deployed environments. Despite this accelerating change, AI education in undergraduate medical education (UME) remains fragmented and largely confined to electives or isolated workshops, creating a readiness gap for future military medical officers. Current approaches do not provide students with the skills necessary to interpret AI-enabled decision-support tools, recognize algorithmic bias, or apply these technologies in austere, bandwidth-limited settings where command authority and operational priorities may shape clinical judgment. This commentary proposes a longitudinal, competency-based framework for integrating AI education across the pre-clerkship, clerkship, and post-clerkship phases of UME. Grounded in the updated 2024 Foundational Competencies for Undergraduate Medical Education, the framework embeds AI-related sub-competencies within existing domains-Patient Care, Systems-Based Practice, Interpersonal and Communication Skills, Professionalism, and others-rather than establishing AI as a standalone topic. A scaffolded progression rooted in Competency Based Medical Education enables students to build foundational knowledge during pre-clerkship, apply AI tools in clinical documentation and decision-making during clerkship, and refine ethical judgment, privacy awareness, and operational readiness as they prepare to transition to graduate medical education. Successful implementation will require intentional curriculum design, faculty development, IT governance, and alignment with existing military training pipelines, including operational medicine field exercises. Embedding AI across the curriculum has direct implications for force readiness. Military medical officers who can evaluate AI outputs, integrate algorithmic tools in austere environments, and communicate uncertainty to patients and line leadership will be better prepared to support casualty care, mission planning, and operational decision-making in large-scale combat operations. Early assessed and sustained competency ensures both patient safety and mission success in future landscapes of war.

Military Medicine
Uniformed Services University of the Health Sciences (US)
Peace, Justice and strong institutions
Openalex Percentile: Top 42%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.