Explainable Artificial Intelligence in Critical Care Nursing: A Discussion Paper.

Area of Science:

• Critical care medicine
• Nursing informatics
• Artificial intelligence ethics

Background:

• Artificial intelligence (AI) is increasingly used in critical care for decision support, but opaque 'black box' systems pose ethical and safety risks.
• Nurses are accountable for patient outcomes, necessitating transparency and interpretability in AI tools used in high-acuity settings.
• Explainable artificial intelligence (XAI) offers a solution by emphasizing transparency, interpretability, and human oversight.

Purpose of the Study:

• To critically discuss Explainable Artificial Intelligence (XAI).
• To examine the relevance and implications of XAI for critical care nursing practice.
• To highlight the importance of XAI in maintaining ethical and professional standards in AI-assisted critical care.

Main Methods:

• Discussion paper.
• Literature review encompassing nursing ethics, professional accountability, critical care practice, and healthcare AI.
• Critical analysis of XAI features like transparency, interpretability, traceability, and contestability.

Main Results:

• XAI's features are vital for critical care nurses to assess, communicate, and justify AI recommendations in high-stakes situations.
• XAI is relevant for clinical decision support, communication, handover, and exercising clinical judgment.
• Challenges include cognitive workload, interpretive competence, workflow integration, governance, and ensuring clinical meaningfulness of explanations.

Conclusions:

• XAI is a professional and ethical requirement for responsible AI use in critical care nursing, not just a technical upgrade.
• Thoughtful XAI implementation can enhance clinical reasoning, transparency, and accountability in patient care.
• Critical care nurses must be key stakeholders in AI development and implementation to preserve autonomy, safety, and patient-centered care.