Addressing the Artificial Intelligence Gap in Nursing Digital Health Readiness

Abstract

We read with great interest the comprehensive study by Dermody et al. (2025) on digital health technology readiness among nurses, which provides valuable insights into the current state of digital preparedness in nursing practice. While the authors thoroughly examined various digital health domains, we wish to highlight a critical dimension that merits additional emphasis: artificial intelligence (AI) readiness in nursing. The study's finding that nurses demonstrated lowest confidence in ‘health smart homes technology’ is particularly concerning when viewed through an AI lens. Smart home technologies increasingly rely on AI-driven predictive analytics, machine learning algorithms for fall detection and automated health monitoring systems (Ronquillo et al. 2021). As healthcare shifts towards AI-powered remote monitoring to support aging in place, nurses' limited confidence in these technologies represents a significant barrier to implementing AI-enhanced care models. The authors noted that ‘patient safety is now inextricably linked to digital transformation’, yet the rapid evolution of AI in healthcare demands even more urgent attention. AI-powered clinical decision support systems, predictive risk algorithms and automated documentation tools are becoming ubiquitous across care settings. Unlike traditional digital health technologies that primarily digitise existing processes, AI fundamentally transforms clinical workflows by introducing autonomous decision-making capabilities that require new forms of digital literacy. The study's identification of age-related differences in digital readiness takes on added significance in the AI context. The qualitative finding that digital technology turns ‘experts into novices’ is amplified when considering AI systems that can challenge clinical judgement or recommend interventions that contradict traditional nursing practice. Older, experienced nurses may face particular challenges in understanding AI algorithms' reasoning processes, potentially leading to decreased trust in AI-assisted care recommendations. We propose that future research should specifically assess AI readiness using validated instruments that measure understanding of machine learning concepts, comfort with AI-generated recommendations and the ability to critically evaluate algorithmic outputs (Yu 2025). The strong correlation between help-seeking and problem-solving behaviours (ρ = 0.91) identified by the authors suggests that nurses with higher AI literacy may be better positioned to collaborate effectively with AI systems while maintaining clinical oversight. The authors' call for ‘shared responsibility for development of digital expertise’ is crucial for AI implementation. Academic-industry partnerships should prioritise AI education that goes beyond basic digital literacy to include algorithmic thinking, bias recognition in AI systems and ethical considerations in AI-assisted decision-making (McGrow 2019). Nursing curricula must evolve to prepare graduates who can work alongside AI as partners rather than passive recipients of algorithmic recommendations. As healthcare organisations increasingly deploy AI solutions for predictive analytics, automated monitoring and clinical decision support, nursing readiness for AI-enhanced practice becomes a patient safety imperative (Wang and Alexander 2016). The foundation established by Dermody et al. provides an excellent starting point for developing AI-specific competency frameworks that ensure nurses remain central to technology-enabled care delivery while maintaining the human connection that defines our profession. Zekai Yu: Writing – original draft and writing – review and editing. The author has read and agreed to the published version of the manuscript. Research Involving Human Participants and/or Animals: This research did not require the involvement of human or animal subjects. Therefore, ethical approval for this study was not required according to local regulations and institutional policies. The author has nothing to report. The author declares no conflicts of interest.

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