Clinical Large Language Model

Clinical large language models (CLLMs) aim to leverage the power of LLMs for processing and extracting information from unstructured clinical text data, such as patient notes and reports, to improve healthcare workflows. Current research focuses on developing and evaluating CLLMs using various architectures, including those incorporating retrieval-augmented generation (RAG) and ensemble reasoning methods, to enhance accuracy and reliability in tasks like cancer staging, medication safety assessment, and clinical note summarization. The successful development and deployment of robust CLLMs holds significant potential for improving clinical decision-making, automating administrative tasks, and ultimately enhancing patient care.