You Know AI Matters for Medicine.
But You Have Nothing to Show for It.
The Clinical AI Portfolio Accelerator. 8 weeks. A real engineer. A deployed system. Proof you can lead.
The Problem
You've sat through AI lectures. You've read the papers. You understand that machine learning is reshaping clinical decision-making.
But when a fellowship interviewer asks what you've built — or when a department chair asks if you can lead an AI initiative — you have no proof. No prototype. No publication. Just concepts.
In medicine, proof is everything. And right now, you don't have it.
What This Is
The Clinical AI Portfolio Accelerator is an 8-week program where you — a clinician — partner with a dedicated engineer to build a real, working clinical AI system from scratch.
You bring the clinical judgment. Your engineering partner brings the code. Together, you produce something neither of you could build alone: a deployable AI application grounded in real clinical reasoning.
No coding required from you. But you're not a passenger — you're the clinical lead.
Your Role as Clinical Lead
- Define the clinical problem and determine what a useful prediction looks like at the bedside
- Set success criteria grounded in clinical relevance — not just model accuracy
- Validate data and features to ensure the model reflects real clinical reasoning
- Evaluate outputs against clinical judgment — when the model flags a patient, does that make sense?
- Co-author the publishable case study documenting your project end-to-end
What You Walk Away With
Your deliverables
- A publishable case study — ready for your CV, a journal submission, or a conference poster
- A live, deployed clinical AI application with a URL you can demo in interviews or grand rounds
- Demonstrated ability to lead cross-functional AI teams — proof you can bridge clinical and technical
- Fluency in the AI development lifecycle — evaluate vendor claims, participate in AI governance, speak credibly
- Practical AI fluency — you'll understand how predictive AI models are built, trained, and evaluated from the inside. Not theory. Enough to lead, evaluate, and speak credibly about clinical AI for the rest of your career.
Choose Your Clinical Use Case
Each clinician-engineer pair selects a real clinical prediction problem:
These are sample use cases to get you started. If you have a clinical prediction problem in your own specialty, bring it — pairs are welcome to define their own use case as long as it's grounded in real predictive AI and clinical data.
Your engineer handles the code. You direct the clinical logic. Together you build the full pipeline: data → model → evaluation → deployment.
8-Week Structure
| Weeks | Focus |
|---|---|
| 1–2 | Define your clinical problem. Select your use case. Explore and prepare the data. |
| 3–4 | Build features. Train your first model. Evaluate outputs against clinical expectations. |
| 5–6 | Refine the model. Run evaluation metrics. Validate clinical relevance. |
| 7 | Deploy your application. Get a live URL. Prepare your case study. |
| 8 | Demo Day — present your system to the cohort. Finalize your publishable case study. |
Time commitment: 4–5 hours/week — 2 hrs live session (weekends) + 2–3 hrs async with your engineer partner.
Who This Is For
- Residents and fellows building a research portfolio for competitive applications
- Attending physicians transitioning into clinical informatics or AI leadership roles
- Any clinician who wants to stop being a spectator in the AI transformation of healthcare
Who's Behind This
1:1 clinician-engineer pairing
Starts June 15, 2026
Application is brief. We select clinicians committed to finishing what they start.
Questions? roofi@gluonlabs.ai