Unstructured Clinical

Unstructured clinical data, such as physician notes and radiology reports, presents a significant challenge in healthcare due to its inherent complexity and lack of standardization. Current research focuses on leveraging natural language processing (NLP) techniques, particularly large language models (LLMs) like BERT and its variants, and retrieval augmented generation (RAG), to extract structured information, predict diagnoses, and improve clinical decision-making. These methods are applied to tasks ranging from identifying specific conditions (e.g., rare diseases, sepsis) to automating administrative tasks like coding and clinical trial enrollment, ultimately aiming to improve patient care and reduce clinician workload. The successful application of these techniques holds substantial promise for enhancing the efficiency and accuracy of healthcare processes.