Contemporary health systems are being squeezed by a triple pandemic: worldwide nursing shortages, the soaring complexity of care fuelled by ageing populations, and the cognitive overload that big data dumps on clinicians. Artificial intelligence (AI) has emerged as the critical lever for driving both efficiency and precision, nowhere more so than at the bedside. “Intelligent nursing” is not the simple plugging-in of technology; it is a systemic re-engineering that weaves machine learning, deep learning, natural language processing and large language model capabilities simultaneously into nursing education, ethics, practice and devices. The goal is to re-sculpt clinical decision-making, streamline care pathways, personalise the patient experience and, ultimately, improve outcomes-moving nursing from an “experience-driven, reactive” craft to a “data-driven, proactive and predictive” discipline and ushering in an era of interdisciplinary, human-machine collaboration.
Core applications of AI empowering clinical practice
AI is reshaping clinical nursing practice, with its core value deriving from two interrelated objectives: enhancing operational efficiency and achieving precision care. By optimizing workflows and reducing cognitive load, it provides a key solution to the persistent challenges of nursing staff shortages and administrative burden. Shepherd and McCarthy emphasise that AI is indispensable for mitigating staffing shortfalls and bureaucratic burden;[
1] Dailah et al. add that automating routine tasks and optimising workload can significantly cut nurses’ psychological stress and burnout, indirectly lifting quality of care.[
2] Topol likewise frames AI-driven workflow improvement as a pillar of “high-performance medicine.”[
3]
Precision care and proactive intervention
The application of artificial intelligence is transforming nursing models from passive response to proactive prevention, a shift fundamentally enabled by the intelligent processing of vast datasets. Esteva et al.’s roadmap on deep learning for medical images and EHRs laid the technical groundwork for real-time risk prediction in nursing;[
4] Jiang et al.’s review confirms AI’s central supportive role in diagnosis and prognosis;[
5] Hassanein et al.’s integrative review further shows that AI-powered predictive analytics help nurses flag high-risk patients early, improving outcomes and exemplifying precision care.[
6]
AI-driven precision nursing technology and tool
Traditional nursing equipment primarily presents data, but AI-integrated smart tools have become “collaborative partners” that can anticipate clinical risks. This transformation extends beyond functionality to redefine the nature of human-machine interaction.
The need for technological upgrade
Conventional monitors display real-time numbers but offer little foresight; nurses drown in data while struggling to decide. Complex interfaces also lengthen training and curb efficiency. AI flips the script: machine-learning algorithms mine data in seconds and push early warnings, while generative interfaces collapse multi-step procedures into one-click actions, returning precious minutes to patients.
Progress to date
According to the latest research reports, smart infusion pumps with micro-flow sensors and AI control algorithms now titrate drip rates using live blood pressure (BP) and heart rate (HR) data and flag impending phlebitis 10 min ahead.[
7] Intelligent beds tiled with flexible pressure-sensor arrays run deep-learning models to forecast pressure ulcer risk, auto-adjust angle and firmness and, with the help of built-in lateral-turn robotic arms, cut stage-I ulcer incidence by roughly one third.[
8] Humanoid nursing robots epitomise the convergence of liberal arts, science, engineering and medicine: carbon-fibre exoskeletons enable 12 kg one-arm lifts; federated learning preserves privacy across multi-site data; clinicians set safety thresholds; literature and design teams script dialect-based, empathetic dialogue that lowers geriatric loneliness scores by 25%.[
9,
10] These robots already deliver drugs, prompt rehabilitation exercises and escort fall-risk patients at night, liberating nurses from repetitive chores. These developments indicate that nursing tools are evolving significantly toward greater intelligence and collaborative capability.
Implications for the nursing workforce and education
As smart tools are integrated into clinical practice, the required competencies of nursing staff and their training systems must also evolve. The complexity of artificial intelligence necessitates that nurses develop digital literacy and critical thinking, creating new imperatives for nursing education.
Interdisciplinary competency demand
The growing complexity of artificial intelligence technology demands new competencies from nursing professionals, centered on developing technological literacy and critical thinking. Accordingly, scholars like Ronquillo et al. stress that systematically integrating AI into nursing requires prioritizing educational innovation and policy guidance. They advocate for a globally collaborative roadmap to restructure curricula, update professional standards, and establish ethical frameworks. Such a structure would support continuous learning and adaptation, enabling nurses to maintain their core professional values within human-machine collaborative care environments.[
11]
Adaptive educational reform
Correspondingly, nursing education requires adaptive reform. Buchanan et al.’s scoping review insists that curricula must be overhauled to include AI basics, data ethics and safe, effective AI use so graduates can thrive in intelligent clinical environments.[
12] Sitterding et al. further propose leveraging AI and gamification to shorten new-nurse transition and sharpen decision-making in complex scenarios.[
13]
A “liberal-arts-science-engineering-medicine” quadruple-adviser micro-major is now piloted in 12 universities: engineers handle robot kinematics and safety thresholds, scientists focus on explainable algorithms and privacy, clinicians define evidence-based metrics, and humanities scholars craft ethical narratives-graduates exit as “nursing orchestrators” who can run robots, decode algorithms and communicate empathetically.
Ethics, safety and trustworthy AI
The sustainable development of AI in nursing fundamentally depends on establishing robust ethical and safety frameworks alongside technological and skill advancement. Ronquillo et al. stress that robust ethical frameworks must tackle data privacy, transparency and algorithmic bias;[
11] Wei et al. identify ethical dilemmas and workforce adaptation as major barriers.[
14] The field is reminded that technological advance must be coupled with fairness and explainability so that the human-centred soul of nursing is not compromised.
For human–machine collaboration, we propose a “3C + 1R” framework: Consent, Compassion, Contestability and Reciprocity—robots must tell patients what they can and cannot do and how to switch them off at will, turning black boxes into negotiable, rejectable conversations.
AI’s reshaping of clinical nursing is irreversible
Evidence and early implementations show that AI, through process optimisation, risk prediction, tool upgrade, educational reform and human–machine synergy, offers a powerful engine for higher-quality, higher-efficiency and more human-centred care.
Yet seamless integration is not automatic. Future work must concentrate on three fronts: (1) Translational research. Move bench-top models and smart devices into evaluable, reimbursable clinical tools that demonstrably improve patient outcomes and nursing efficiency; (2) Ethical governance. Build a robust “trustworthy AI” framework guaranteeing fairness, explainability and professional autonomy; (3) Educational investment. Continuously re-engineer nursing curricula to cultivate graduates fluent in human–machine collaboration and interdisciplinary communication.
The global race in nursing is shifting from “how many more nurses” to “how deeply we can integrate liberal arts, science, engineering and medicine.” Only when engineers can build it, scientists can compute it, clinicians can apply it and humanists can explain it will we unleash the full caring potential of smart devices and humanoid robots—extending nurses’ hands, amplifying nursing’s value and safeguarding human dignity.
The Author(s) 2026. This article is published with open access at journal.hep.com.cn.
PDF
(185KB)
