Clinical Language Understanding

Clinical Language Understanding (CLU) focuses on enabling computers to accurately process and interpret clinical text data, aiming to improve healthcare efficiency and patient outcomes. Current research emphasizes developing and evaluating large language models (LLMs) for CLU tasks like information extraction, question answering, and clinical coding, exploring techniques such as enhanced tokenization and prompting strategies to improve performance. However, studies reveal that simply fine-tuning general-purpose LLMs on biomedical data doesn't always guarantee superior performance on unseen clinical data, highlighting the need for more sophisticated approaches and rigorous benchmarking, such as the CLUE benchmark, to facilitate comparison and reproducibility. The ultimate goal is to leverage CLU advancements for applications like automated report generation, improved diagnostics, and more efficient clinical workflows.