Most skin cancer AI models are trained on perfect, high-resolution images taken by dermatologists in a clinic. But in the real world, people just take pictures of their skin with their phones, which are often blurry, poorly lit, or off-centered. This lightning talk is about building an AI screening tool that actually works with the messy photos regular people take.

I will be talking about using the new SLICE-3D dataset (from the ISIC 2024 Grand Challenge) to train a machine learning model that classifies skin lesions. I'll cover my process for tackling the gap between clinical data and smartphone images, including exploring lightweight models like CNNs with transfer learning and quantized Vision Transformers, and discussing the trade-offs between cloud deployment and keeping it as a local, privacy-first tool.

Finally, I will show a quick live demo of an easy-to-use web interface for the model. The goal isn't to replace doctors, but to create a fast, unintimidating pre-screening app that can look at a phone picture and tell a user, "Hey, this looks irregular, you should probably go see a dermatologist."