Electronic Health Records (EHRs) are a treasure trove of patient information, holding the key to better diagnoses, treatments, and healthcare outcomes. But there's a problem: EHRs are complex, voluminous, and often filled with unstructured data like clinical notes, making them difficult to analyze effectively. Enter Large Language Models (LLMs), the AI powerhouses transforming how we interact with language. A new scoping review examined hundreds of research papers to understand how LLMs are being used to unlock the potential of EHRs. The review revealed a surge in LLM-EHR research, particularly after the release of ChatGPT, with leading universities and medical centers at the forefront. These studies explore a range of applications, from extracting key information like social determinants of health and family history to summarizing complex medical reports into patient-friendly language. LLMs are also being used to improve disease diagnosis and prediction by analyzing patient histories and identifying patterns that might indicate future health risks. Imagine an AI that can quickly summarize a patient's medical history, flag potential drug interactions, or even translate medical jargon into plain English for patients. This is the promise of LLMs in healthcare. However, the review also highlights challenges, including the need for more open-source EHR data, cost-effective fine-tuning techniques, and addressing ethical concerns like patient privacy and bias in AI algorithms. The future of healthcare may depend on how well we can harness the power of LLMs while navigating these important ethical considerations. As AI continues to evolve, its role in deciphering the complexities of medical data will only grow, potentially leading to a new era of personalized and proactive healthcare.