# SecondLook

- **Event:** [Built with Claude: Life Sciences](https://cerebralvalley.ai/e/built-with-claude-life-sciences)
- **When:** Jul 7 at 12:00 PM – Jul 14 at 12:00 AM (EDT)
- **Where:** Online
- **Team:** [Edward Yi](https://cerebralvalley.ai/u/aiedwardyi)
- **GitHub:** https://github.com/aiedwardyi/SecondLook
- **Demo video:** https://youtu.be/v3zw5bIx1yw
- **Gallery:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery
- **Page:** https://cerebralvalley.ai/e/built-with-claude-life-sciences/hackathon/gallery/90

SecondLook is a trust layer for an AI basal cell carcinoma detector, built for a dermatopathologist, Mohs surgeon, or dermatologist reading skin-pathology slides under real clinic load, where long days and fatigue nudge the hardest calls. AI support on a cancer-or-not call only helps if the doctor can trust it, and a confident wrong answer is worse than none.

I trained EfficientNet-B4 detectors for basal cell carcinoma and built an app that scores regions of a slide, shows a Grad-CAM heatmap of where the model looked, and sorts each score into positive, uncertain, or negative. On a confident positive, Claude reads the heatmap and attention metrics and returns VERIFIED, FLAGGED, or DEFER. Claude never diagnoses and never changes the detector's call; it checks whether the model looked at the right place. The doctor keeps the final say.

What I found: strong test scores cannot separate a trustworthy model from an unreliable one. Two models with near-identical scores can be looking at tissue or at a blank corner, and Claude tells them apart. It runs today as a live, open-source app, turning a detector a doctor could not trust into one they can.

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