# Calibrate

- **Event:** [Meta Global AI Developer Hackathon](https://cerebralvalley.ai/e/meta-ai-developer-hackathon)
- **When:** Oct 26, 12:00 PM – Nov 4, 9:00 PM (EST)
- **Where:** Online
- **Team:** [Marissa Famularo](https://cerebralvalley.ai/u/caivory03)
- **GitHub:** https://github.com/MarissaFamularo/Calibrate
- **Demo video:** https://www.youtube.com/watch?v=OkVZH2gPSVg
- **Gallery:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1270

Calibrate is a clinical simulation that measures how doctors in training rely on AI: when they trust it, when they override it, and whether they were right.

The problem: AI is already built into the EMR and the ambient scribes residents and doctors use every day, yet there is no standard way to know whether a resident uses it safely. Some clinicians trust it for everything; others won’t touch it. Neither is safe. The skill that matters is calibrated trust: recognizing where each tool is reliable and where it fails, and acting on that. Residency programs have no way to observe or assess it.

The product: A resident works a realistic vascular case (a diabetic patient with a toe wound and limb-threatening ischemia). They interview the patient, order tests, sign notes, and work alongside an ambient AI assistant that is right most of the time and wrong at specific, pre-defined points. The resident can type or dictate. The case branches on their decisions, and outcomes follow: catch the bad ABI alert and the wound heals; trust it and the patient loses a toe.

At the end, residents and faculty get a calibration profile: appropriate reliance, overreliance, appropriate override, and reflexive dismissal, with a faculty view that compares residents side by side. Every role runs on Meta’s open-weights Muse Spark Open, behind a deterministic case engine that owns the clinical truth.

## More from Meta Global AI Developer Hackathon

- [Aftershock](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1267)
- [Aurif](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1268)
- [Aegis Home](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1269)
- [Pausebird](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1271)
- [AgentSpace](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1272)
- [SustainAfrica LLc](https://cerebralvalley.ai/e/meta-ai-developer-hackathon/hackathon/gallery/1273)

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