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

Daniel Joseph E Berdida^1,2

¹Nursing Department, North Private College of Nursing, Arar, Northern Border, Saudi Arabia.

Nursing in Critical Care

|July 6, 2026

Summary

This summary is machine-generated.

Explainable artificial intelligence (XAI) is crucial for critical care nursing, ensuring transparency and accountability in AI use. It empowers nurses to ethically manage AI-driven insights for better patient care.

Keywords:

artificial intelligence clinical decision support clinical judgement critical care nursing explainable artificial intelligence human oversight patient safety professional accountability

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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.