Retaining Human Connectedness in Increasingly <scp>AI</scp> Driven Environments

Abstract

Nearly 40 years ago a question was posed that, while radical for its time, remains important today: is a science of caring possible? (Dunlop 1986). This paper was a significant and thought-provoking contribution to nursing scholarship that reflected the intellectual and sociopolitical climate of the day. It came at a time when nursing was seeking legitimacy as an academic and scientific discipline and during a period of increased feminist critique and reassessment of conventional knowledge systems. Dunlop (1986) wrote the paper to consider the compatibility of caring to science. She questioned the current dominant paradigms by challenging the idea that caring, a concept central to nursing, could be wholly contained within the rigid and conventional bounds of scientific rationalism. While it was a provocation shaped by its time, almost 40 years later the question persists because as new technologies emerge, old questions resurface in new forms. In her paper, Dunlop (1986) differentiated between a science for caring, which applied extant scientific knowledge to nursing practice, and a science of caring, which sought to understand caring itself, and position it as a phenomenon worthy of systematic inquiry in its own right. Dunlop (1986) proposed that a true science of caring would likely require a hermeneutic or interpretive (rather than a purely empirical) approach. Positioning nursing within wider social struggles around the legitimacy of women's work, Dunlop (1986) critiqued institutional structures that tended to obscure or devalue the labour and the skill associated with caring work. As such, Dunlop (1986) provided not only a theoretical exploration of caring but also a nuanced and thoughtful reflection on the gender politics and epistemological debates that shaped nursing in the mid-1980s. In current healthcare environments increasingly dominated by metrics, technology, and task-oriented efficiencies, Dunlop's argument that caring is not simply reducible to behaviours that can be easily measured or standardised feels more relevant than ever. Today's nurses are practicing in a context that, like the one Dunlop wrote about in the 1980s, is momentous, though markedly different (from that of the 1980's) and involving previously unimagined technologies. It is a context increasingly shaped by emergent technologies, many of them driven by artificial intelligence (AI), such as machine learning, large language models, text mining, predictive algorithms, and clinical decision support systems. These technologies carry promises of objectivity, effectiveness, precision, and foresight, and are contributing to a shifting landscape in which nursing's ways of knowing and practicing are being redefined and extended (Ronquillo et al. 2021). There is increasing interest in how AI can support clinical practice, with a quick snapshot of recent research showing a range of projects including potential for the use of AI in staging pressure injuries (Karaçay et al. 2025); investigations into how multimodal large language models might reduce nurses' documentation workload while improving the effectiveness and quality of patient care (Michalowski et al. 2025), and the viability of using a generative AI tool as a supportive resource for nursing practice (Saban and Dubovi 2024). Clearly, AI-driven technologies bring very exciting possibilities to nursing practice and to wider healthcare (Ronquillo et al. 2021); but as with the earlier tensions between caring and science, these new technologies do represent some potential threats to some aspects of nursing epistemics and practice. Today, new questions loom in relation to nursing epistemology and practice: How can clinical judgement—judgement that is shaped by years of clinical practice, recognition and interpretation of subtle observations, and embodied attentiveness to cues—meaningfully coexist with the predictions of machines trained on enormous datasets? What happens when a nurse's intuitive feelings about a patient conflict with AI-generated predictions? How can clinical judgement and machine learning effectively work together? These questions are not simply practical; they are also ethical and epistemological. AI is here to stay and ignoring it is not an option. The questions we are currently facing in nursing today are not if new technology should be used in healthcare, but how it should be used, and what must not be lost in the process. As AI continues to develop and evolve in complexity, it will become increasingly integrated into healthcare. Rather than resist it, our challenge is to effectively engage with and leverage its capabilities to benefit patient care and to support a strong, robust and effective nursing workforce. As Ronquillo et al. (2021) have emphasised, it is essential that nurses play an active role in shaping how AI-driven technologies are implemented in clinical settings. Just as Dunlop (1986) questioned whether caring could be systematised, we now ask whether the judgement, presence, and combination of education, experience, and skill that nurses bring to the bedside can thrive in a clinical world increasingly shaped by big data, machine learning, large language sets, dashboards, algorithms, and decision trees. Echoing Dunlop's (1986) concern during the rise of scientific empiricism in the 1980s, today's dominance of digital systems and algorithmic logic does pose a threat to what makes nursing unique and distinctive, placing it at risk of being diminished and marginalised because of the rise in new digitised systems of knowledge. The core of nursing has always involved so much more than the mechanistic application of procedures and protocols, and the performing of discrete clinical tasks. Nurses value interpersonal connection, and recognise the significance of presence, human connection, and mutual respect in their relationships with patients and families. In daily practice, nurses draw on advanced theoretical and experiential knowledge to engage meaningfully with patients and families, responding not only to clinical cues but also to spoken and unspoken needs, gestures, and contextual nuances. A major strength of nursing lies in a commitment to relational and personalised care and a deep concern with how care is delivered, not just what is done. Nurses nurture meaningful connections with patients and families and prioritise interactions such as listening, noticing, responding with empathy, and recognising the uniqueness and the humanity of people, beyond the clinical issue or diagnoses. As nurses we must embrace technology and new ways of knowing and embed them into practice to better serve individuals and communities and to strengthen our practice. However, with the emergence of new AI-driven technologies becoming increasingly embedded in clinical practice, we must guard against a drift toward technocratic care that sidelines the human dimensions of nursing. We must remain vigilant and actively participate in decisions about which AI-driven technologies are adopted and how they are implemented (Ronquillo et al. 2021), asking critical questions and thoughtfully guiding their integration into practice to ensure they enhance health systems and benefit both service users and providers, without compromising the core values and distinctive essence of nursing. As AI becomes more integrated into healthcare, there is a risk that its capabilities could be seen as a substitute for expert nursing judgement, rather than a support for it. While technology can enhance efficiency, it cannot replace the depth of knowledge, critical thinking, and relational expertise that skilled and experienced nurses bring to patient care. It is essential to ensure that AI supports, rather than displaces, the vital role of nursing in achieving safe, high-quality outcomes for patients and their families. The unique and distinctive contributions of nursing must remain visible, valued, and uncompromised. The tools we draw on in practice will continue to evolve, but the core values and the human connectedness that are central elements to nursing practice must remain. Despite technological advancement, it is crucial that nurses retain the ability to wholistically assess, think critically, question, interpret, and advocate for patient benefit. These skills are essential to safeguarding patient care, upholding ethical standards, and preserving the humanity that lies at the heart of nursing practice. Just as scientific empiricism was never intended to replace the skill and the artistry that is embedded in nursing practice, our engagement with technology, including AI driven tools, must never eclipse our embodied, ethical, and skilled human knowing. The challenge is not to choose between the human dimensions of care and emerging technologies, but to ensure that the gaze of the nurse—the educated, informed, attentive, compassionate, and very human gaze—continues to be recognised and valued in healthcare landscapes increasingly shaped by AI and the digitised platforms it powers. The author declares no conflicts of interest. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/jan.70041. The author has nothing to report.

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