The current developments in the research area of natural language processing (NLP) within the medical and digital library domains are notably advancing the field through innovative applications of large language models (LLMs). A significant trend is the shift towards leveraging LLMs for complex tasks such as long-form medical question answering, information extraction from clinical notes, and automated medical coding. These advancements are not only enhancing the accuracy and efficiency of these tasks but also addressing practical challenges such as computational resource requirements and the need for human expert annotations. Notably, the integration of LLMs into digital library workflows for textual data processing is expanding the capabilities of these systems, enabling more sophisticated and scalable data management and analysis. The field is also witnessing the development of specialized tools and benchmarks that facilitate the deployment and evaluation of LLMs in these contexts, ensuring that the models are both effective and reliable. These developments collectively underscore a move towards more sophisticated, automated, and human-aligned NLP solutions in critical domains.