USU's AI Roadmap: Preparing Military Medical Education for the Next Decade
Medical knowledge is doubling every few months. Uniformed Services University of the Health Sciences (USU) is treating that speed as a design constraint, not a headline. A team of faculty and School of Medicine Class of 2027 students co-authored a clear plan to weave artificial intelligence into military medical education-so future clinicians can safely partner with AI in complex, far-forward environments.
Their article in Military Medicine, "Transforming Military Healthcare Education and Training: AI Integration for Future Readiness," is led by Air Force Lt. Col. (Dr.) Justin Peacock and Dr. Rebekah Cole, with contributions from Air Force Lt. Col. (Dr.) Joshua Duncan, Dr. Anita Samuel, and students Air Force 2nd Lt. Brandon Jensen and Army 2nd Lt. Brad Snively. The premise is simple: clinicians don't need to be data scientists. They need to be AI-literate professionals who know when, where, and how to apply these tools-while keeping humans in the loop.
Why this matters for educators
Future conflicts will stress the Military Health System (MHS) with austere conditions, limited support, and crushing information loads. AI-enabled tools will sit beside medics, residents, and attending physicians as decision support, logistics aides, and documentation assistants. If education lags, care quality and readiness suffer.
For medical schools, GME programs, and teaching hospitals, this roadmap offers a workable plan to build AI literacy without bloating the curriculum. It emphasizes ethics, safety, and practical use at the point of care.
What the roadmap covers
- Faculty-first approach: build faculty competence so they can confidently teach, supervise, and assess AI use.
- Curriculum threading: introduce AI concepts early and incrementally-from M1 to residency-rather than as a one-off module.
- Ethics and policy: teach bias, transparency limits, data stewardship, and approval pathways alongside clinical application.
- Clinical integration: practice with realistic workflows, cases, and operational scenarios (including low-resource settings).
- Partnerships: connect with industry and research teams to keep content current and grounded in real tools.
Student-led change in action
USU students are co-authors and co-designers, not passive recipients. Air Force 2nd Lt. Brandon Jensen uses AI daily to clarify hard concepts, generate practice questions, and improve retention. During an internal medicine rotation, AI-assisted reasoning helped surface a key diagnosis that could have been missed.
Army 2nd Lt. Brad Snively emphasizes the "cognitive offload" value: AI can summarize dense histories, build comparison tables, and fill small knowledge gaps on the floor. Offloading busywork gives students and residents more space for patient communication and clinical judgment.
AI as a force multiplier for the MHS
- Reduced cognitive burden: automate documentation and data synthesis so clinicians can spend time where it matters-on the patient.
- Operational readiness: train with AI in austere conditions to sustain care quality away from full hospital infrastructure.
- Personalized learning: use AI to adapt study plans, cases, and assessments to individual needs without adding instructor load.
Known risks and the guardrails that matter
Black-box behavior remains a real issue-some systems produce answers without transparent reasoning. That's why the roadmap centers on verification, reference-checking, and a firm human-in-the-loop standard.
Students and faculty should treat AI output like a first draft: useful, but incomplete until validated against clinical guidelines, peer-reviewed sources, and sound judgment. Ethical principles from defense and public health bodies provide helpful anchors for curriculum and policy.
Practical steps for educators and program leaders
- Stand up a faculty AI cohort: monthly workshops on prompt quality, bias, validation, and case-based use in specialties.
- Thread AI across courses: short, repeatable exercises (e.g., differential diagnosis drafts, guideline lookups, SOAP note summaries).
- Assess the process, not just the answer: grade how learners use AI, cite sources, and verify output.
- Simulate operational care: practice with connectivity limits, sensor noise, and triage at scale.
- Set clear policies: what tools are allowed, documentation expectations, and how to disclose AI assistance.
- Build a reference spine: link AI use to established standards and journals; require in-text citations when AI is involved.
- Create student leadership roles: appoint AI stewards to pilot tools, gather feedback, and co-write training materials.
Assessment ideas you can deploy this term
- AI-augmented case write-up: students generate a draft plan with AI, annotate every claim with sources, then revise without AI.
- Bias check drill: compare outputs across two AI tools for a case with demographic variability; document discrepancies.
- Operational scenario: limited bandwidth, incomplete vitals, time pressure; learners decide if/when AI adds value and why.
What success looks like
Faculty can explain when to use AI, how to verify it, and how to document its role. Students engage AI to speed up synthesis while improving accuracy and accountability. Clinical teams maintain high standards-even far forward-because workflows were practiced before deployment.
Above all, AI shifts work from administrative noise to human presence, without sacrificing safety or ethics.
For further learning
USU contributors
Air Force Lt. Col. (Dr.) Justin Peacock; Dr. Rebekah Cole; Air Force Lt. Col. (Dr.) Joshua Duncan; Dr. Anita Samuel; Air Force 2nd Lt. Brandon Jensen; Army 2nd Lt. Brad Snively.
The shared message is clear: integrate AI literacy across medical education, keep humans accountable, and train for the realities of modern care-classroom, clinic, and battlefield.
Your membership also unlocks:
