Clinical Text

Clinical text analysis focuses on extracting meaningful information from unstructured medical records to improve healthcare. Current research emphasizes leveraging large language models (LLMs), such as BERT and its variants, along with techniques like retrieval-augmented generation (RAG) and parameter-efficient fine-tuning (PEFT), to enhance tasks like entity recognition, information retrieval, and phenotyping. These advancements are crucial for automating high-throughput phenotyping, improving diagnostic accuracy, and facilitating more efficient clinical decision-making, ultimately impacting patient care and medical research. The development of open-source tools and datasets is also a significant trend, fostering collaboration and reproducibility within the field.