Imagine an AI that could instantly answer complex medical questions, providing doctors with fast, reliable information to improve patient care. That's the promise of Large Language Models (LLMs) like LLaMA-2 and Mistral. However, using these powerful AI models for medical advice isn't as simple as just asking them questions. They need specialized training to understand the nuances of medical language and avoid giving inaccurate or outdated information. Researchers are tackling this challenge by developing sophisticated fine-tuning techniques. One exciting innovation is rsDoRA+, a method that combines the strengths of several existing approaches to make LLMs learn medical knowledge more efficiently and reliably. It's like giving the AI a crash course in medical school! Another key development is ReRAG (Retrieval on Demand and Question Rewrite). This technique allows the AI to access and process relevant medical information in real time, similar to how a doctor might consult a medical database. Even more impressively, if the AI doesn’t understand a question at first, ReRAG helps it rephrase the query to find the most relevant information. Early results are promising. Tests show that combining rsDoRA+ and ReRAG dramatically improves the accuracy and richness of the AI's medical answers. This research suggests that fine-tuned LLMs could revolutionize how healthcare professionals access vital information, leading to faster diagnoses and better treatment decisions. While challenges remain, including keeping the AI's knowledge up-to-date and ensuring patient privacy, these advancements represent a significant step toward a future where AI plays a crucial role in improving healthcare for everyone.