# Enhancing Nursing and Elderly Care with Large Language Models: An AI-Driven Framework

arXiv: https://arxiv.org/abs/2412.09946 · HTML: https://mlsanity.com/paper/2412.09946/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 13 Dec 2024.

## Builds on (most cited first, 5 of 5)

- [Improved Baselines with Visual Instruction Tuning](https://mlsanity.com/paper/2310.03744/) (2023)
- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [Language Model Meets Prototypes: Towards Interpretable Text Classification Models through Prototypical Networks](https://mlsanity.com/paper/2412.03761/) (2024)
- [GAProtoNet: A Multi-head Graph Attention-based Prototypical Network for Interpretable Text Classification](https://mlsanity.com/paper/2409.13312/) (2024)
- [MedNgage: A Dataset for Understanding Engagement in Patient-Nurse Conversations](https://mlsanity.com/paper/2305.19981/) (2023)

## Built on by (most cited first, 2 of 2)

- [Learning Primitive Embodied World Models: Towards Scalable Robotic Learning](https://mlsanity.com/paper/2508.20840/) (2025)
- [ChatWise: A Strategy-Guided Chatbot for Enhancing Cognitive Support in Older Adults](https://mlsanity.com/paper/2503.05740/) (2025)

## Abstract (arXiv)

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.