Biomedical Natural Language Processing

Biomedical Natural Language Processing (BioNLP) aims to develop computational methods for extracting knowledge and insights from biomedical text, such as medical records and research papers. Current research heavily utilizes large language models (LLMs) like BERT and GPT, often fine-tuned or instruction-tuned for specific tasks like named entity recognition, relation extraction, and question answering, sometimes enhanced by techniques like ensemble methods or adversarial training to improve robustness. These advancements are crucial for accelerating biomedical research, improving clinical decision-making, and facilitating the development of more effective healthcare systems by enabling efficient analysis of vast amounts of textual data.