Ethical Imperatives for Retrieval-Augmented Generation in Clinical Nursing: Viewpoint on Responsible AI Use.

Area of Science:

• Artificial Intelligence in Healthcare
• Clinical Nursing Informatics
• Medical Ethics

Background:

• Retrieval-augmented generation (RAG) enhances large language models (LLMs) with real-time external knowledge.
• RAG adoption is increasing in medical research and clinical settings.
• Ethical considerations are paramount for RAG implementation in patient care.

Purpose of the Study:

• To explore ethical imperatives for RAG systems in clinical nursing.
• To examine ethical risks of RAG-enhanced LLMs in nursing practice.
• To propose guidelines for responsible RAG implementation in healthcare.

Main Methods:

• Structured analysis of ethical considerations in RAG for nursing.
• Review of key principles: accuracy, fairness, transparency, accountability, and human oversight.
• Discussion of data governance and explainable AI (XAI) techniques.

Main Results:

• RAG systems present ethical challenges in clinical nursing environments.
• Ensuring accuracy, fairness, transparency, and accountability is crucial.
• Human oversight remains essential for safe and effective RAG use.

Conclusions:

• Robust data governance, XAI, and continuous monitoring are vital for responsible RAG implementation.
• Collaboration among healthcare professionals, AI developers, and policymakers is necessary.
• AI, including RAG, can support patient safety, reduce disparities, and improve nursing care quality.