Transforming nursing with large language models: from concept to practice.

Area of Science:

• Nursing Informatics
• Artificial Intelligence in Healthcare

Background:

• Large language models (LLMs) show promise for transforming nursing practices, including patient education, diagnostic support, and administrative efficiency.
• The integration of LLMs in healthcare presents challenges such as AI hallucination and data privacy concerns.

Purpose of the Study:

• To explore the potential applications of LLMs in nursing.
• To identify and address the challenges associated with implementing LLMs in clinical settings.
• To propose strategies for refining LLM accuracy and ensuring data security.

Main Methods:

• Review of current literature on LLM applications in nursing.
• Analysis of proposed methodologies for mitigating LLM limitations, including prompt engineering, temperature adjustments, model fine-tuning, and local deployment.
• Discussion of the ethical considerations and limitations of LLMs in patient care.

Main Results:

• LLMs can enhance patient education, assist in diagnostics, provide treatment recommendations, and improve administrative task efficiency in nursing.
• Strategies like prompt engineering, temperature adjustments, model fine-tuning, and local deployment can help improve LLM accuracy and data security.
• Despite advancements, LLMs cannot replace the critical expertise and judgment of human healthcare professionals.

Conclusions:

• LLMs hold transformative potential for nursing, offering benefits in various clinical and administrative areas.
• Addressing AI hallucination and data privacy is crucial for the responsible adoption of LLMs in healthcare.
• A synergistic approach, combining LLM capabilities with human expertise, is essential for optimal patient care.