In the healthcare sector, a significant amount of unstructured textual data emerges daily, necessitating efficient extraction and structuring to enhance care quality, clinical decision-making, and research advancement. Natural Language Processing (NLP) techniques, evolving from rule-based to deep learning approaches, are pivotal in tasks like event extraction and clinical trial matching. The rise of large language models (LLMs) is transforming health NLP, showcasing robust capabilities in text generation and understanding. This study analyzed 19,123 research studies, identifying distinct task distributions between LLMs and traditional NLP methods. Key findings included LLMs excelling in open-ended tasks like medical education and mental health, while traditional methods maintained strengths in structured information extraction, such as electronic health records. The rapid growth of LLM applications suggests a shift towards a hybrid approach, integrating both technologies for enhanced healthcare solutions. Continued advancements in LLMs promise substantial impacts on clinical reasoning, decision support, and AI regulation in healthcare.
Source link
