Doctors face a tough challenge: sifting through massive, complex electronic medical records (EMRs) to diagnose illnesses. A new AI framework called medIKAL aims to make this process easier. It combines the power of large language models (LLMs)—think along the lines of ChatGPT—with the structured knowledge of medical knowledge graphs. Imagine an LLM that can read a patient's EMR, understand the key symptoms, and then cross-reference that information with a vast database of medical knowledge. That's the core idea behind medIKAL. Instead of treating all information equally, medIKAL assigns different levels of importance to different types of entities within the EMR. For instance, current symptoms are given more weight than past surgical history. This helps the LLM focus on the most relevant information when searching for possible diagnoses in the knowledge graph. What's really innovative is how medIKAL uses a "residual network" approach. The LLM makes an initial diagnosis on its own. Then, this initial guess is combined with the information retrieved from the knowledge graph, acting as a check and balance system. A path-based ranking algorithm helps refine the diagnosis further, considering how closely related the symptoms and other information are to different candidate diseases. Finally, a fill-in-the-blank style prompt template helps the LLM reason through the possibilities and even correct its own errors. This makes the AI's diagnostic process more transparent and reliable. Tests on a new Chinese EMR dataset show promising results. medIKAL could be a game-changer, helping doctors quickly and accurately diagnose diseases, especially in complex cases. While still in its early stages, this kind of AI-assisted diagnosis has the potential to improve healthcare for everyone. Future work on medIKAL will focus on incorporating numerical data like lab results more effectively and making the system more robust when dealing with limited patient information. But for now, it represents a significant step toward more efficient and effective disease diagnosis using AI.