Medical Reports Simplification Using Large Language Models

Making medical reports easily understandable for a wider audience is a significant endeavor, and the recent advancements in deep learning and large language models offer a promising solution. In our research, we have introduced a fine-tuned Text-to-Text Transformer (T5) model to efficiently summarize these medical reports. Our model is trained and assessed on the publicly accessible Indiana Dataset. To gauge its effectiveness, we employ the ROUGE metrics. The outcomes we've achieved hold promise for the potential of this approach in enhancing medical report summarization. By harnessing the power of cutting-edge technology, we aim to make medical information more accessible and comprehensible to everyone.