Advancements in Natural Language Processing (NLP) have improved the extraction of information from unstructured biomedical text, particularly in recognizing clinical named entities like diseases, genes, and proteins. This study evaluates the performance of Bi-LSTM and Bi-LSTM-CRF models for Clinical Named Entity Recognition (CNER) using three benchmark datasets: NCBI-Disease, BC2GM, and JNLPBA.…