Energy Sustainability in Artificial Intelligence for Nursing Practice: Addressing the Hidden Cost - Yen-Ching Chang; Agung Budi Prasetio (Viva Medika, 2026)
The adoption of artificial intelligence in nursing practice has accelerated rapidly and offers substantial benefits in terms of efficiency, predictive accuracy, and clinical workflow optimization. Applications such as automated documentation, natural language processing of clinical notes, and decision support systems are increasingly embedded in routine nursing activities. While these technologies enhance performance and productivity, growing evidence indicates that artificial intelligence systems are associated with significant energy consumption during model training, data storage, and operational deployment. The healthcare sector already contributes a measurable proportion of global greenhouse gas emissions, and energy intensive digital infrastructures further amplify this burden. Training advanced artificial intelligence models may generate substantial carbon emissions, and repeated inference processes in daily clinical use accumulate additional energy demand.Despite these concerns, current evaluation frameworks for artificial intelligence in nursing remain primarily centered on clinical effectiveness, usability, safety, and organizational readiness. Energy consumption, carbon footprint, and broader ecological implications are rarely incorporated into technology assessment processes. This omission creates a critical gap between digital innovation and environmental responsibility within nursing informatics. This short communication synthesizes available evidence on the hidden energy costs of artificial intelligence in healthcare and nursing contexts, identifies structural gaps in prevailing evaluation paradigms, and proposes the integration of standardized sustainability metrics. The proposed framework emphasizes explicit reporting of energy consumption, carbon emissions, and life cycle environmental impacts alongside traditional clinical and operational indicators. By reframing artificial intelligence evaluation through a sustainability lens, nursing can contribute to advancing digital transformation that is not only safe and effective but also environmentally responsible