The field of clinical natural language processing is rapidly advancing, with a focus on developing innovative solutions for extracting structured information from unstructured clinical text. Recent research has highlighted the potential of large language models (LLMs) in achieving state-of-the-art performance in various clinical NLP tasks, including named entity recognition, question answering, and text classification. Notably, LLMs have been shown to be effective in identifying adherence to clinical reporting guidelines, extracting medical insights from electronic health records, and extending medical ontologies from clinical notes. However, challenges remain, such as addressing the limitations of traditional evaluation metrics, ensuring patient privacy, and developing more equitable and culturally-aware medical technologies. Some noteworthy papers in this area include Evaluating Open-Weight Large Language Models for Structured Data Extraction from Narrative Medical Reports, which demonstrated the effectiveness of LLMs in extracting structured data from clinical reports across multiple languages and institutions. Additionally, MedPath introduced a large-scale biomedical entity linking dataset that enables the development of more accurate and interpretable clinical NLP models.