Sharing knowledge between hospitals is crucial for better patient care. Imagine if doctors could pool their collective experience to diagnose illnesses faster and more accurately, especially in cases with limited data. This is the promise of transfer learning in healthcare, where insights from one hospital's data can be applied to another. However, medical language can vary greatly between hospitals, making it difficult to directly apply these insights. This new research explores how to overcome these challenges using Large Language Models (LLMs) like those powering tools like ChatGPT. Researchers analyzed electronic health records from two different hospital systems, looking specifically at influenza cases. They wanted to see how well LLMs could help bridge the gap between different medical vocabularies. Their findings reveal that LLMs trained specifically on medical data, like Med-BERT, outshine more general models when it comes to understanding and transferring medical insights. While general LLMs can be helpful, they need careful adjustments to match the specific language of each hospital. Interestingly, fine-tuning—a common technique to adapt AI models—can sometimes hinder the performance of clinically trained LLMs, suggesting that medical expertise embedded within these models is already quite robust. This research provides valuable insights into how we can build smarter AI systems for healthcare that can learn from multiple sources and improve patient care across different hospital settings. Future work involves exploring more advanced techniques to combine the strengths of different LLMs, making it even easier to share knowledge while preserving patient privacy.