Performance Analysis of Large Language Models for Medical Text Summarization

Due to the rapid expansion of medical literature, keeping pace with the latest research and clinical guidelines has become more challenging for healthcare professionals.To overcome this challenge, effective text summarization is crucial for improving access to knowledge, enhancing clinical decision-making, and ultimately benefiting patient outcomes. In this study, a medical text summarization system that employs large language models (LLMs) was fine-tuned and evaluated with the objective of generating precise, logical, and brief summaries of medical literature, emphasizing clinical relevance and ease of understanding. We plan to evaluate the performance of GPT3, GPT4 and the fine-tuned T5, BART, and Pegasus models trained on the standard PubMed dataset using standard evaluation metrics.