1. IntroductionThe rapid digitization of healthcare records has led to an exponential increase in the volume of medical reports generated daily. Physicians, researchers, and healthcare providers must analyze these reports quickly to make informed clinical decisions. However, the sheer amount of textual data presents challenges in terms of processing time, information retrieval, and cognitive overload. Traditional summarization techniques, such as rule-based and statistical methods, often fall short in generating concise, contextually accurate summaries. Recent advancements in Natural Language Processing (NLP), particularly with transformer-based models like BERT, T5, and GPT, have demonstrated remarkable performance in text summarization tasks. Leveraging these models in a real-time, cloud-based framework can significantly enhance medical report summarization, improving efficiency and decision-making in healthcare environments.