Northern Medical Center · Professional Education

AI in Health Care: From Models to Medicine

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Duration

10 Weeks

3–5 hours per week

Format

Three-Track

Build + Judge + Deploy

Next Cohort

Spring 2026

Now enrolling

Investment

$500 – $1,200

Scholarships available

Overview

This 10-week program serves three audiences: students learning AI foundations, health care professionals evaluating clinical AI tools, and leaders making adoption decisions. Our three-track design brings all audiences into one cohort — building the shared language that makes real clinical AI deployment possible.

“We don’t teach doctors to code or coders to diagnose — we build the shared language between them.”

Learning Objectives

  • 1Understand the fundamentals of machine learning, deep learning, and large language models in clinical context
  • 2Evaluate AI tools for clinical deployment using decision curves, fairness metrics, and implementation science
  • 3Navigate FDA SaMD regulatory pathways, HIPAA compliance, and algorithmic bias assessment
  • 4Design or critique an AI implementation plan for a health care organization
  • 5Read and critically appraise medical AI literature using structured frameworks
  • 6Bridge the communication gap between technical teams and clinical stakeholders

Three Tracks, One Cohort

Build

Track A — AI Principles

Code-First

  • Who: CS, pre-med, STEM undergrads
  • Focus: Hands-on ML/DL with medical datasets
  • Tools: Colab, PyTorch, Claude Code, OpenClaw
  • Capstone: Build and evaluate a clinical AI model
Judge

Track B — Clinical Applications

Evaluate & Apply

  • Who: Residents, nurses, pharmacists, researchers
  • Focus: AI evaluation and deployment readiness
  • Tools: No-code templates, paper critique frameworks
  • Capstone: Clinical utility memo or deployment critique
Deploy

Track C — Executive

Decide & Deploy

  • Who: Department heads, CMOs, innovation teams
  • Focus: Governance, procurement, ROI, adoption
  • Tools: ROI calculators, vendor evaluation matrix
  • Capstone: Board-ready AI strategy presentation

A → B → C: Track A builds models, Track B judges them, Track C decides whether to deploy them.

Core Modules

01

AI/ML Fundamentals

Supervised learning, evaluation metrics, bias-variance

02

Deep Learning

CNN, Transformer, embeddings for clinical data

03

LLM & Generative AI

Prompting, RAG, hallucination, clinical NLP

04

Medical Data

EHR, imaging, omics, wearables, FHIR

05

Regulation & Ethics

FDA SaMD, HIPAA, EU AI Act, bias, liability

06

Model Evaluation

Beyond AUROC — decision curves, NNT, clinical utility

Program Tiers

Multiple entry points to match your goals and schedule.

Tier 1

Free Webinars

Free

  • 60–90 min live sessions
  • Latest paper spotlights
  • Medical AI case breakdowns
  • Community access

Tier 2

Cohort Course

$500–$1,200

  • 10-week structured program
  • Three-track assignments
  • Paper critique workshops
  • Final capstone project
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Tier 3

Professional Workshop

$3K–$8K

  • 2-day or 4-week hybrid
  • AI governance deep-dive
  • ROI & vendor evaluation
  • Institutional partnership

Living Curriculum

Every week features a curated paper from three categories — our moat against static MOOCs.

Method Papers

New models, benchmarks, evaluation frameworks

Clinical Validation

Real-world deployment, prospective studies

Critical Analysis

Bias, data shift, hallucination, failure modes

“The most common failure in medical AI education is confusing model performance with clinical value.”

How We Compare

Most programs fall into two camps: technical bootcamps or executive overviews. We bridge both — and add a clinical judgment track that doesn’t exist elsewhere.

FeatureOursCoursera / StanfordHarvard / JHU
Three-track system (Build/Judge/Deploy)
Shared anchor cases across tracks
Non-coding clinical judgment track
Weekly live paper critiques
Living curriculum (updated each cohort)
Hands-on coding with medical datasets
Executive governance & ROI track
Cross-disciplinary cohort breakouts

Frequently Asked Questions

Do I need coding experience?
Track A involves hands-on coding with Python and Colab. Track B and C require zero coding — they focus on evaluation, judgment, and decision-making.
Can I switch tracks mid-course?
Yes. The three tracks share core modules, so switching is straightforward through Week 3. After that, assignments diverge but you can still audit other tracks.
Is this accredited for CME/CE credits?
Not yet. We’re launching as a non-credit certificate program first. CME partnerships are planned for future cohorts.
How is this different from Coursera or edX courses?
Those are self-paced recordings. We’re a live cohort with weekly paper critiques, case discussions, and cross-disciplinary breakout sessions. Our living curriculum updates every cohort.
What if I’m a complete beginner in both AI and medicine?
Track A is designed for exactly that — pre-med and STEM undergrads who want to learn AI through medical use cases. No prior ML or clinical experience required.
What’s the time commitment?
3–5 hours per week for 10 weeks: one live session (~90 min), one case lab (~60 min), plus assignments and paper reading.

What Learners Say

“First cohort testimonials will appear here after Spring 2026.”

Coming Soon

Track A Student

“We look forward to sharing the experiences of our first clinical learners.”

Coming Soon

Track B Clinician

“Executive track feedback will be featured after the inaugural cohort.”

Coming Soon

Track C Leader

Testimonials from our first cohort coming Spring 2026.

Ready to Bridge the Gap?

Spring 2026 cohort now forming. 10 weeks, 3–5 hours per week. All three tracks welcome.

Contact to Enroll