What if a radiologist facing a complex scan in the middle of the night could ask an AI assistant for a second opinion, right from their local workstation? This isn't science fiction; it's the frontier of Clinical AI, made possible by models like Google's powerful and efficient Gemma 3n. Unlike massive cloud-based systems, Gemma 3n is designed for on-device deployment, making it perfect for the secure, private environments required in medicine. But how do you turn a generalist Vision-Language Model (VLM) into a medical expert? This guide provides a complete, hands-on walkthrough of the Fine-Tuning Gemma 3n process. Using the high-performance Unsloth library and Hugging Face TRL, we transform the base model into a specialized tool for Medical VQA. We'll show you the dramatic before-and-after results, as the model learns to shift from making confident but incorrect diagnoses to providing the precise, objective descriptions required by clinical experts. Dive into the code and discover how to build a practical, next-generation Medical AI.