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

backgroundArtificial intelligence (AI) is increasingly embedded in critical care environments to support clinical decision-making, risk prediction and workflow optimisation. However, many AI systems operate as opaque 'black boxes', raising ethical, professional and safety concerns in high-acuity settings where nurses remain accountable for patient outcomes. Explainable artificial intelligence (XAI) has emerged in response to these concerns by emphasising transparency, interpretability and human oversight.

aimTo critically discuss XAI and examine its relevance and implications for critical care nursing practice. STUDY

designDiscussion paper informed by literature on nursing ethics, professional accountability, critical care practice and healthcare AI.

resultsThis paper examines XAI as an approach to clinical AI that foregrounds transparency, interpretability, traceability and contestability. It argues that these features are especially important in critical care nursing, where nurses must assess, communicate, justify and, when necessary, challenge AI-informed recommendations in rapidly changing and high-stakes clinical situations. The paper discusses the relevance of XAI to clinical decision support, communication, handover and the exercise of clinical judgement and considers challenges related to cognitive workload, interpretive competence, workflow integration, governance and implementation. It further argues that the value of XAI in critical care depends not only on technical explainability but also on whether explanations are clinically meaningful and usable in bedside practice.

conclusionXAI should be understood not simply as a technical enhancement, but as a professional and ethical requirement for the responsible use of AI in critical care nursing. When implemented thoughtfully, XAI can strengthen clinical reasoning, transparency and accountable care. RELEVANCE TO CLINICAL PRACTICE: Critical care nurses should be recognised essential stakeholders in the design, implementation, governance, education and policy development of clinical AI systems. Embedding explainability into AI is central to preserving nursing autonomy, professional accountability, patient safety and patient-centred care in technologically advanced critical care environments.