Few-Shot and Zero-Shot Biomedical Named Entity Recognition: A Procedure for Enhancing BioBERT with Prompt-Based Learning and Large Language Models

Biomedical Named Entity Recognition (NER) plays a crucial role in extracting valuable information from clinical texts and biomedical literature. Traditional deep learning models, including BioBERT, ClinicalBERT, and RoBERTa, have demonstrated substantial improvements in NER tasks; however, they still struggle with low-resource biomedical terms, domain adaptation challenges, and unseen entity generalization. To address these limitations, this study introduces a hybrid framework combining BioBERT, prompt-based learning, and Large Language Models (LLMs) to enhance few-shot and zero-shot learning capabilities in biomedical NER. The proposed model effectively leverages contextual knowledge from pre-trained transformers, reducing dependency on extensive labelled datasets while maintaining high accuracy. After extensive experimental evaluation across multiple biomedical benchmark datasets, the proposed approach demonstrates consistently strong performance. Specifically, it achieves 89.8% accuracy, 88.7% precision, 90.5% recall, and an F1-score of 0.895, outperforming baseline methods and demonstrating its effectiveness for biomedical text mining tasks. Compared with other methods, prompt engineering helps the model adapt much better to the unique language of biomedical texts. Plus, boosting with a large language model (LLM) gives NER performance a serious lift by picking up on tricky semantic and grammatical details. These findings demonstrate the efficacy of few-shot and zero-shot learning in biomedical text mining, paving the way for better automated clinical data analysis and smarter decision-support systems.