Imagine an AI that can generate perfectly tailored medical summaries for any specialist, cutting through the noise of lengthy medical records and highlighting the most relevant information. That's the vision behind new research that leverages 'ontologies' – structured knowledge bases – to enhance AI's ability to produce accurate and domain-specific medical summaries. Electronic Health Records (EHRs) are notorious for their length and complexity, often contributing to clinician burnout. While Large Language Models (LLMs) offer a potential solution for automating summarization, they sometimes generate inaccurate or irrelevant information (aka 'hallucinations'). This new research tackles this challenge head-on by using ontologies to constrain the AI's output, ensuring the summaries align with established medical knowledge. Researchers tested this approach on the MIMIC-III dataset, a vast collection of real-world clinical notes. The results? A significant reduction in hallucinations and improved accuracy in generating domain-specific summaries, allowing doctors to focus on the most crucial information. This approach isn't just about making summaries shorter; it's about tailoring them to different specialties. For example, a cardiologist needs different information than an oncologist. By using ontologies, the AI can automatically create summaries focused on the relevant concepts for each domain. While promising, this approach faces challenges. The current method involves multiple computational steps, making it resource-intensive. However, this is a significant stride towards harnessing the power of AI to combat information overload in healthcare and empower clinicians with the precise knowledge they need, when they need it. Further research aims to streamline the process and incorporate human feedback for even greater accuracy and usability.