Imagine a team of AI doctors, each specializing in different areas of medicine, consulting on a patient's case. This isn't science fiction, but the reality of ColaCare, a groundbreaking framework using large language models (LLMs) to revolutionize how we analyze electronic health records (EHRs). EHRs are treasure troves of patient data, but traditional AI struggles to understand the nuances within them. ColaCare changes this with its unique multi-agent approach, simulating a real-life medical consultation. In ColaCare, "DoctorAgents," powered by specialized expert models, analyze numerical EHR data like lab results and vital signs. They then present their findings to a "MetaAgent," a central LLM acting as the lead consultant. This MetaAgent synthesizes the information, guiding the DoctorAgents through a collaborative discussion, much like a multidisciplinary team meeting in a hospital. What sets ColaCare apart is its ability to integrate external medical knowledge. Using a retrieval-augmented generation (RAG) module, the AI doctors can access up-to-date medical guidelines and research, ensuring their decisions are based on the latest evidence. The results are impressive. Tested on four major EHR datasets, ColaCare outperformed existing models in predicting patient mortality. It's not just about better predictions, though. The collaborative process generates a detailed, human-readable report, explaining the AI's reasoning and the evidence behind it. This transparency is crucial for building trust and allowing human doctors to understand and validate the AI's conclusions. ColaCare offers a glimpse into the future of healthcare—one where AI and humans work together, combining the best of both worlds to provide more accurate, personalized, and ultimately, better care. While still in its early stages, ColaCare's collaborative approach offers a promising path towards more intelligent and interpretable AI in medicine, paving the way for a new era of precision medicine.