Medical Text

Medical text analysis leverages natural language processing (NLP) and large language models (LLMs) to extract meaningful information from clinical notes, research papers, and other medical documents, aiming to improve healthcare efficiency and decision-making. Current research focuses on enhancing LLMs' accuracy and reliability for tasks like diagnosis prediction, risk factor identification, and patient summarization, often employing techniques like knowledge graph integration, retrieval-augmented generation, and multi-agent systems. This field is significant because it promises to automate time-consuming tasks, improve the quality of medical information, and ultimately enhance patient care and medical research.