From Edge to Clinic: Training Medical Diagnostics on Consumer Hardware

This project demonstrates that sophisticated medical AI doesn't require enterprise infrastructure—consumer laptops with GPU acceleration can now train clinical-grade diagnostic tools. By addressing real-world challenges like class imbalance through weighted sampling and transfer learning with ResNet-18, the author built a 96%-accurate pneumonia detector without massive datasets or expensive servers. This shift brings accessible, AI-powered medical diagnostics closer to individual developers and smaller organizations, redefining the economics of medical AI development.