Enhancing Medical Text Summarization using Transformer-Based NLP Models for Clinical Decision Support

Medical text summarization plays a crucial role in clinical decision support by enabling healthcare professionals to quickly access essential information from vast amounts of unstructured medical texts. With the rapid advancements in Natural Language Processing (NLP), transformer-based models have emerged as powerful tools for generating high-quality summaries. This paper investigates the effectiveness of state-of-the-art transformer models, such as BERT, GPT, and T5, in summarizing medical texts while preserving critical information. We conduct comprehensive evaluations using benchmark datasets and assess the performance of these models in terms of coherence, relevance, and readability. The experimental results demonstrate that transformer-based models significantly outperform traditional extractive and abstractive summarization techniques, offering more accurate and contextually meaningful summaries. Furthermore, we highlight the importance of domain-specific pretraining and fine-tuning to enhance model performance in medical applications. This study provides valuable insights into the practical deployment of transformer-based summarization models in healthcare settings, ultimately contributing to improved clinical workflows and informed decision-making.