Biomedical Natural Language Processing in the Era of Large Language Models
Naoto Usuyama, Cliff Wong, Sheng Zhang, Tristan Naumann, Hoifung Poon
Abstract
Biomedicine has rapidly digitized over recent decades, from genomic sequencing to electronic medical records. Now, the rise of large language models (LLMs) is driving a generative artificial intelligence (AI) revolution in natural language processing (NLP). Together, these trends create unprecedented possibilities to optimize patient care and accelerate biomedical discovery. Biomedical NLP already boosts productivity by automating labor-intensive tasks such as knowledge extraction and medical abstraction. Emerging approaches promise creativity gain, surpassing standard healthcare practices and uncovering emergent capabilities through Web-scale biomedical knowledge and population-level patient data. However, LLMs remain prone to hallucinations and omissions, and ensuring compliance and safety is vital in order to do no harm. Incorporating diverse modalities such as imaging and genomics is also essential for comprehensive solutions. We review these challenges and opportunities in biomedical NLP, offering historical context, surveying the current state of the art, and exploring frontiers for AI researchers and biomedical practitioners.