Palliative Care Nurses Weigh Promise and Perils of AI at Life’s End

Artificial intelligence is moving into nearly every corner of modern medicine, from radiology suites to emergency triage desks. But one of the most ethically charged settings — the palliative care ward, where nurses accompany patients through the final chapter of life — has remained largely untouched by the technology. A new qualitative study published in BMC Nursing offers a rare, ground-level view of what happens when the people who provide end-of-life care are asked to imagine algorithms entering that intimate space. The verdict from eighteen experienced palliative and end-of-life nurses is neither utopian nor dismissive: they see genuine potential in AI, but they also see ethical fault lines that could crack open if the technology is deployed carelessly.

The research team, led by Yanan Xu and Li Zhang of Bengbu Medical University together with colleagues from Nanjing Drum Tower Hospital and the First Affiliated Hospital of Nanjing Medical University, conducted semi-structured interviews with eighteen nurses recruited through purposive sampling — a technique that deliberately selects participants with direct, relevant experience rather than drawing them at random. The interviews were systematically coded, with researchers organizing recurring ideas into themes, a standard qualitative method that transforms hours of conversation into a structured map of professional opinion. The study was reported according to the Consolidated Criteria for Reporting Qualitative Research, or COREQ, a 32-item checklist designed to make qualitative studies transparent and reproducible. Ethical approval came from the Ethics Committee of Gulou Hospital, Nanjing University School of Medicine, and every participant gave informed consent before taking part.

Four main themes emerged from the analysis, and together they read like a blueprint for how AI might — and might not — belong at the bedside of the dying. The first theme was recognition of AI’s potential value. The second concerned the real-world challenges that stand in the way of integration. The third captured ethical worries about privacy, fairness, and responsibility. The fourth described ethical tensions embedded in nursing practice itself. That structure matters because it separates practical barriers from moral ones, a distinction that engineers and hospital administrators often blur when they pitch new clinical tools.

On the practical side, the nurses saw AI as potentially useful in three concrete domains: workload support, symptom assessment, and communication. Anyone who has spent time in palliative care knows why these three came first. Nurses in this field juggle complex medication regimens, frequent reassessments of pain and breathlessness, and emotionally exhausting conversations with patients and families — often simultaneously. An algorithm that could automate documentation, flag subtle changes in a patient’s symptom trajectory, or help draft clear explanations for anxious relatives would free up the scarcest resource in end-of-life care: the nurse’s undivided attention. Symptom assessment is a particularly promising target, because palliative symptoms such as pain, dyspnea, and delirium fluctuate in ways that are easy to miss between rounds, and machine learning models excel at detecting patterns in longitudinal data that humans overlook.

Yet the nurses were equally clear-eyed about the obstacles. They questioned whether current AI systems could adapt to the realities of palliative care, where patients’ conditions change rapidly and where the goals of care often shift from cure to comfort. They also flagged a skills gap: AI literacy among nursing staff is uneven, and a tool that requires technical fluency the workforce does not possess risks becoming shelfware or, worse, a source of error. This concern echoes a broader finding in health informatics — that the success of clinical AI depends less on the sophistication of the model than on the fit between the model and the clinical workflow, and on the training of the people expected to use it.

The ethical concerns were the study’s most striking contribution, and they cluster around three concepts: privacy, fairness, and responsibility. Privacy is self-evident in a setting where patients share their fears, family conflicts, and end-of-life wishes with their caregivers; the nurses worried about where that information goes once it feeds a machine-learning system, who can access it, and whether consent obtained in a hospital corridor can truly cover algorithmic processing of the most personal data a person will ever disclose. Fairness concerns arise because AI models trained on data from certain populations can perform poorly for others — older patients, people with rare conditions, or those whose symptom expression differs from the training data — potentially compounding inequities at the very moment of life when no one can afford to be underserved.

Responsibility proved the thorniest issue of all. If an AI system misjudges a symptom or recommends an inappropriate intervention, who answers for it — the nurse who followed the recommendation, the clinician who deployed the system, the vendor who built it, or the hospital that purchased it? The nurses raised questions about accountability and autonomy that map directly onto an unresolved debate in AI governance: the problem of meaningful human control. In palliative care, where decisions carry irreversible weight, the nurses’ insistence that a human must remain in the loop is not technophobia; it is a demand that responsibility remain assignable to a person, not dissolved into an opaque statistical process.

Beneath these practical and governance concerns lies a deeper tension the nurses described in their own practice: the preservation of humanistic care. Palliative nursing is built on presence, touch, listening, and the willingness to sit with suffering that cannot be fixed. The participants worried that an efficiency-driven technology, however well-intentioned, could erode precisely the qualities that make end-of-life care humane — that a screen between nurse and patient might substitute transaction for relationship. This is the study’s viral insight, the one that should stop every AI developer mid-pitch: in this setting, the highest-value feature of any technology is that it gives nurses more time for the human work, not that it performs the human work itself.

The study’s findings arrive at a moment when healthcare systems worldwide are racing to integrate AI into clinical practice, often with governance frameworks that lag behind deployment. By documenting the views of nurses — the largest workforce in healthcare and the group in closest continuous contact with patients — the research provides evidence for future AI design, training, and governance in palliative care. The authors suggest their results can inform how developers build tools that respect the realities of end-of-life settings, how educators prepare nursing staff to use them competently, and how institutions write policies that assign responsibility clearly and protect patient privacy and fairness.

The study has limits worth noting. Eighteen nurses in a single country cannot speak for every palliative care setting, and qualitative descriptive studies capture perceptions rather than measured outcomes — the nurses’ views on AI’s potential are hypotheses to be tested, not evidence of what AI can actually achieve at the bedside. Still, as the first systematic exploration of this question in end-of-life nursing, the research fills a conspicuous gap. The message from the people who hold dying patients’ hands is ultimately a challenge to the AI industry: bring your algorithms into palliative care if you must, but design them to serve human connection, prove they are fair and private, and never ask them to replace the human presence that patients at the end of life need most.

Subject of Research: Palliative care nurses' views on AI and ethical dilemmas

Article Title: Exploring palliative care nurses’ views on the potential of AI and ethical dilemmas: a qualitative study

Article References: Xu, Y., Jing, J., Yan, Z., Ji, X., Zhou, Y., & Zhang, L. (2026). Exploring palliative care nurses’ views on the potential of AI and ethical dilemmas: a qualitative study. BMC Nursing. https://doi.org/10.1186/s12912-026-05318-z

Image Credits: AI Generated

DOI: 10.1186/s12912-026-05318-z

Keywords: artificial intelligence, palliative care, nursing, medical ethics, healthcare technology, qualitative research, Exploring, palliative, care, nurses, views, potential