Beyond Automation: How Generative <scp>AI</scp> Is Transforming Nursing Practice

Abstract

This is a theoretical discussion of the author's belief that generative AI actively reshapes the nurse's role. While the role of the nurse has evolved over centuries from ancient caregiving practices to wartime care and, more recently, to technology-driven care (Bullough and Bullough 1979, 4), discussions rarely focus on how technological advancements shape the nursing role itself. Integrating generative AI in healthcare settings challenges traditional notions of what it means to be a nurse. Nursing has embraced many artificial intelligence (AI) technological innovations in the 21st century thus far to support care, such as risk-based alerts and predictive analytics and workflows, according to O'Connor et al. (2022). Generative AI has advanced these capabilities further by creating new information from text or images that nurses could use for direct and indirect patient care. For example, AI can generate patient leaflet information and innovative personalised care plans (Liao et al. 2024), suggesting that these traditionally nursing-related tasks now have an augmented nursing “touch”. Generative AI could also impact the notion of empathy as part of a nurse's caregiving role, and a rising concern that AI-simulated empathy may lack the genuine emotional response that can occur from a nurse providing care Shen et al. (2024). Contrasting against this view are Sakumoto and Joshi (2023), who advise that simulated empathic responses from generative AI assistants could manage routine and/or repetitive communications, enabling nurses to maintain deeper empathic engagement for more complex responses. The nurse's role in this situation would then move from an action of empathy towards all patient scenarios to “deploying” empathy selectively, depending on the nature of the case. There may be a concern with this idea. However, being aware of burnout and empathic and moral impact as part of the increasing workload is impacting the nursing profession, and generative AI could be seen as a solution to this (Liao et al. 2024). Beyond the impact on empathy in nursing, generative AI is also shaping nursing practice, influencing how care is delivered and how the nurse–patient relationship is structured. While traditional AI supports nursing practice by requiring nurses to input and verify clinical information, generative AI extends into core nursing roles and functions, such as therapeutic relationships and patient-centred care. Unlike traditional AI, which operates under nurse supervision to assist with clinical decision-making, generative AI can reshape the nurse–patient dynamic by introducing a human-machine interaction (Ktistakis and Bourbakis 2018). This shift is already evident in applications such as medical robots and empathic virtual assistants, which can assist with both diagnostic and emotional aspects of care. As generative AI continues to evolve, it is no longer just a tool but a co-developer in nursing-based tasks. Nurses are no longer the sole creators of care plans. Instead, engaging with generative AI recommendations blurs the boundary between nurse-led and AI-led practice. This challenges traditional notions of nursing expertise if generative AI can develop person-centred care plans or suggest personalised medications by analysing patient data and chemical medication compositions (Shork 2020). Moreover, generative AI's ability to integrate research and evidence-based knowledge beyond a nurse's specialism could support solutions that shorten waiting times for specialist care, further transforming the nurse's role and extending care provision. Using Posthumanism theory as a lens, this role shift in nursing can be understood as emerging from the interconnectedness between humans, technology, and institutions over time (Lin and Zhao 2025). This means that the nurse's role is not solely inherent to the individual but learned and crafted through institutions such as universities, employment in healthcare and technology these institutio