AI RESEARCH
Beyond the Basics: Leveraging Large Language Model for Fine-Grained Medical Entity Recognition
arXiv CS.AI
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ArXi:2604.17214v1 Announce Type: new Extracting clinically relevant information from unstructured medical narratives such as admission notes, discharge summaries, and emergency case histories remains a challenge in clinical natural language processing (NLP). Medical Entity Recognition (MER) identifies meaningful concepts embedded in these records. Recent advancements in large language models (LLMs) have shown competitive MER performance; however, evaluations often focus on general entity types, offering limited utility for real-world clinical needs requiring finer-grained extraction.