Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework
It addresses the growing demands of healthcare for aging populations by providing AI-driven solutions for patient monitoring and interaction, though it is incremental in its approach.
This paper tackled the problem of applying large language models (LLMs) to nursing and elderly care by developing an AI-driven framework, resulting in significant improvements in performance for specialized tasks through incremental pre-training and supervised fine-tuning on a novel Chinese nursing dataset.
This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chinese nursing dataset and implement incremental pre-training (IPT) and supervised fine-tuning (SFT) techniques to enhance LLM performance in specialized tasks. Using LangChain, we develop a dynamic nursing assistant capable of real-time care and personalized interventions. Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations.