Empowering Care: Transforming Nursing Through Artificial Intelligence

3.1 Clinical decision support and predictive analytics

AI-powered CDSS and predictive analytics using ML models have become invaluable in modern healthcare settings. By leveraging vast amounts of structured and unstructured patient data, these systems assist nurses in making evidence-based decisions in real time. AI-driven algorithms analyze electronic health records (EHRs), vital signs, lab results, and patient histories to detect early indicators of critical conditions such as sepsis, cardiac events, and respiratory distress—all of which require rapid intervention to prevent severe complications [1]. CDSS enhances patient safety by providing predictive insights that alert healthcare professionals to subtle physiological changes before symptoms escalate. For instance, AI-based early warning systems for sepsis detection have been shown to outperform traditional manual screening methods, reducing mortality rates and ICU admissions [11, 12]. Similarly, AI-assisted cardiac monitoring integrates ML with ECG analysis to predict arrhythmias and myocardial infarctions earlier than conventional diagnostic tools, enabling preemptive treatment strategies [13, 14, 15]. The application of AI in respiratory monitoring has also demonstrated significant potential. AI-powered wearable devices continuously track oxygen saturation, breathing patterns, and lung function to detect early signs of respiratory distress in patients with chronic conditions such as COPD or post-COVID syndromes [16]. These systems use deep learning algorithms to identify anomalies, improving the efficiency of interventions and reducing hospital readmissions [16].

AI can be used for risk prediction models so nurses can prioritize preventive strategies in a timely manner. For example, AI models process patient histories, mobility data, and nursing records to assess fall risk [17], and deep-learning models for pressure injury prediction analyze skin integrity, hemodynamic data, and mobility factors to classify patients based on risk levels [18, 19]. Similarly, AI-driven systems for adverse drug reaction detection use reinforcement learning techniques to monitor medication safety and enhance drug monitoring practices in clinical settings [20]. A notable real-world example is the Mayo Clinic’s recent implementation of an AI-powered clinical documentation platform across its healthcare system [21].

These data-driven approaches enhance patient safety and enable more efficient resource allocation. By prioritizing high-risk patients, nurses can allocate care resources more effectively, ensuring timely and tailored interventions to individual needs. For example, ML-driven predictive models for delirium risk have demonstrated strong performance in intensive care units, identifying high-risk pediatric and geriatric patients [22]. In oncology and palliative care, AI-driven models assist in predicting care needs for advanced cancer patients receiving chemotherapy, providing personalized treatment plans, and improving quality of life [23, 24].

As AI technology advances, CDSS and predictive analysis will continue to refine clinical decision-making, reduce the cognitive burden on nurses, and enhance patient outcomes through proactive and personalized care. However, ongoing research, education, and policy frameworks are essential to maximize AI’s benefits while mitigating potential risks.

3.2 Remote patient monitoring and telehealth

Telehealth has emerged as a critical modality for delivering care, particularly in remote and underserved areas, where access to healthcare professionals is often limited [25, 26]. The integration of AI in telehealth platforms has significantly enhanced remote patient monitoring by enabling real-time analysis of data collected from wearable devices, home monitoring systems, and teleconsultation services [27]. This continuous data stream allows nurses and healthcare providers to detect subtle physiological changes in a patient’s condition, facilitating timely interventions even when patients are not physically present in a healthcare facility [28]. One significant application of AI in telehealth is AI-powered remote monitoring, which tracks vital parameters such as blood glucose levels, blood pressure, oxygen saturation, and heart rate using continuous sensor-based devices. These systems employ ML algorithms to detect abnormal patterns and provide real-time alerts to nurses and healthcare teams when a patient’s readings indicate a potential complication [29, 30, 31]. Other studies have demonstrated that AI can analyze remote ECG and blood pressure data, predict potential exacerbations, and alert providers to intervene before a patient experiences severe symptoms [32, 33, 34]. This proactive approach helps reduce hospital readmissions and emergency department visits, as early warnings allow for preemptive adjustments in treatment plans.

3.3 Virtual assistants and Chatbots

AI-driven virtual assistants are essential in chronic disease management as they reinforce medication adherence, monitor symptoms, and provide mental health support [35, 36, 37]. These tools deliver real-time, personalized health information and self-care guidance, fostering patient engagement, adherence to treatment plans, and overall satisfaction with remote care. Some specialized chatbots support specific clinical populations (i.e., oncology patients and heart failure patients), can effectively assist with symptom assessment, promote emotional well-being, and provide treatment guidance [35, 38, 39], which can empower patients to manage their health and reduce nurses’ administrative workload, enabling nurses to focus more on direct patient care. Emerging applications of AI-powered VAs are seen through the creative use of virtual humanoids in “coaching” older adults in the compliance and performance of the recommended health-enhancing physical activities [40, 41, 42].

AI-powered chatbots have been integrated into telehealth platforms to assist with continuous patient support, optimize clinical workflows, and improve accessibility to care. These chatbots, powered by NLP and ML, assist with routine patient inquiries, symptom tracking, and health education. The continuous accessibility of AI-powered chatbots benefits individuals in remote areas or those requiring frequent monitoring and support [28]. The 24/7 availability of AI-powered systems ensures that patients receive timely responses to their concerns, reducing the burden on healthcare providers while improving patient satisfaction [35]. This level of accessibility is particularly beneficial for patients in remote areas or those managing chronic conditions who require regular monitoring and guidance. Moreover, automated reminders, motivational coaching, and real-time feedback help patients stay engaged in self-care while allowing nurses to dedicate more time to complex clinical tasks and direct patient interactions [4]. By integrating these technologies, healthcare teams can enhance efficiency while maintaining high-quality, patient-centered care [43].

Nurses play a critical role in integrating AI into clinical practice by interpreting and validating AI-generated recommendations to meet individual patient needs [44]. As patient advocates, nurses guide individuals in understanding AI-generated recommendations, ensuring that care remains personalized and ethically sound [45, 46]. While AI-driven innovations improve efficiency and patient outcomes, their integration into nursing workflows presents challenges such as user acceptance, data accuracy, algorithm reliability, and privacy concerns. Ongoing validation is essential to ensure AI recommendations align with clinical judgment [28, 35]. As remote healthcare expands, nurses remain at the forefront, bridging the gap between technology and human connection, ensuring AI complements compassionate, patient-centered care [44, 45, 46].

3.4 Robotics and automation

Robotic systems, created under the artificial super intelligence (ASI), are being developed and becoming mainstream in the industry. Interestingly, in nursing, robots in various shapes and sizes are already integrated into practice areas to assist with routine clinical tasks, allowing nurses to focus on complex decision-making and direct patient care. For instance, the “Design Spectrum of Healthcare Robots for Older Adults” summarizes the various types of robots that are being used in gero-technology [47]. From automated medication dispensing to robotic-assisted mobility support, these technologies are revolutionizing healthcare delivery and enhancing patient safety [48]. Automated medication management is one of the most impactful robotics applications in nursing. Medication errors remain a significant concern in healthcare, contributing to adverse drug events and increased hospitalizations [27]. Robotic dispensing systems and automated medication carts help reduce human error by ensuring precise dosage calculations and distribution. These systems are integrated with EHRs to cross-check prescriptions, enhancing patient safety and reducing nurses’ administrative burden [49, 50].

In addition to medication management, robotic-assisted mobility devices play a crucial role in patient handling and rehabilitation. Exoskeletons and robotic lift systems assist nurses in repositioning and mobilizing patients, reducing the risk of musculoskeletal injuries among nurses. These technologies promote patient mobility and faster recovery and help alleviate the physical strain on nursing staff [48, 51]. Social and companion robots are also gaining traction in geriatric and long-term care settings. Studies show that robots can help reduce loneliness, anxiety, and depression while promoting social engagement and cognitive stimulation [52, 53, 54]. In hospital environments, autonomous service robots are used for non-clinical tasks such as transporting supplies, disinfecting patient rooms, and delivering meals. These robots use AI-driven navigation systems and can efficiently perform repetitive tasks, allowing nurses to dedicate more time to patient care [48]. Robotic nurse assistant systems have been particularly valuable in supporting vital sign measurements and item delivery [55].

3.5 Nursing research, informatics, and data analytics

AI applications in nursing research highlight the creative synergy between data, analytics, visualization, and informatics. Nursing informatics, as a specialized field in nursing, integrates nursing science, computer science, and information science to manage and communicate data, information, and knowledge in nursing practice [46]. As AI technology and big data analytics advance, nursing informatics professionals play a crucial role in ensuring that these innovations are effectively integrated into healthcare systems, optimizing patient-centered care, enhanced clinical workflows, and supported by evidence-based decision-making. Big data analytics significantly impacts the research process in areas of hospital operations, resource allocation, and workforce management by enabling predictive modeling for patient admissions, identifying seasonal fluctuations in healthcare demand, and optimizing staffing levels to maintain adequate nurse-to-patient ratios [56]. Workload-balancing algorithms further enhance efficiency by analyzing real-time patient acuity levels, ensuring equitable distribution of nursing assignments, reducing burnout, and improving care delivery [57].

AI-powered informatics is an emerging concept that can be maximized in evidence-based practice and research that are contributory to quality assurance, improvement, and patient safety. This mechanism is through the analysis of large-scale health data and patient attributes from various sources to detect patterns in treatment outcomes, medication errors, and adverse events. By leveraging these insights, healthcare organizations can refine best practices, minimize hospital-acquired conditions, and enhance compliance with safety protocols [58, 59]. Additionally, continuous monitoring of performance metrics through AI-driven informatics enables healthcare teams to benchmark against evidence-based standards and implement data-informed strategies that improve clinical effectiveness [60]. Another critical aspect of AI-driven nursing informatics is its ability to analyze unstructured data from nurse documentation, clinical notes, and incident reports. The NLP technologies extract valuable insights from these records, identifying trends related to patient symptoms, safety concerns, and workflow inefficiencies, which inform decision-making and enhance communication within healthcare settings [39, 45].

As big data analytics become increasingly embedded in nursing practice, nurses must develop data and research literacies and informatics competencies to effectively interpret AI-generated insights, produce knowledge, and translate them in clinical and operational contexts [61, 62]. Interestingly, nursing curricula and training programs are incorporating informatics courses to help prepare nurses for the evolving role of AI, big data, analytics, and research in healthcare [46]. Interestingly, research laboratories powered by AI technologies to develop and pilot nursing applications (e.g., Multiple Applications in Reality-Virtuality Experience Laboratory or MARVEL) are also emerging [42]. By fostering a workforce proficient in data and research-driven decision-making, nursing informatics professionals can lead these technologies’ ethical and responsible implementation, ensuring that patient-centered care remains the foundation of healthcare innovation.

3.6 Education, training, and simulation applications

Integrating AI into nursing education transforms how students and practicing nurses acquire, refine, and expand their clinical competencies. AI-powered tools facilitate personalized learning, enhance critical thinking, and provide real-time feedback, ensuring that nursing students and professionals stay current with advancements in healthcare. As nursing practice evolves with emerging technologies and evidence-based approaches, AI-driven educational platforms support lifelong learning and continuous professional development [44]. One of the most impactful applications of AI in nursing education is the use of adaptive learning platforms that tailor educational content based on individual progress, strengths, and knowledge gaps. Examples of such systems include Unbound Medicine’s Nursing Central with Assist, PrepU by Wolters Kluwer, Nursify AI, Smart Sparrow, and Person’s AI learning tools, which offer personalized study plans and adaptive quizzes, can adjust coursework and quiz difficulty based on students’ performance, allowing for targeted reinforcement of weaker areas, and customize study plans and practice exams to prepare students for the licensure exam more effectively [63]. Additionally, the use of generative AI (GAI) applications are creative approaches to visualize concepts, create topic maps, and summarize learning content in an instant.

Another example is a machine learning-based simulation that provides real-time feedback and adaptive case studies, fostering nursing students’ critical thinking and decision-making skills [64]. Platforms such as CureFun and 3DiTeams use large language models to simulate patient-nurse interactions, offering realistic scenarios and instant feedback to improve diagnostic reasoning and communication. AI-powered simulation technologies such as pediatric assessments [65], AI-driven virtual patient scenarios [66], and virtual reality (VR)-assisted procedures involving routine clinical tasks (i.e., intravenous insertion and wound care) have proven to enhance student engagement, problem-solving, and clinical accuracy [67, 68, 69, 70]. Other forms of extended reality (XR) technologies, such as augmented reality (AR) and mixed reality (MR) applications projected through head-mounted displays (HMDs), have the potential to immerse nurse learners in a variety of case scenarios with virtual patients as clients. These innovations ensure that nursing students and professionals acquire essential skills confidently, ultimately enhancing patient care outcomes.

Beyond pre-licensure education, AI-driven solutions for nursing competency development provide ongoing support for practicing nurses through personalized, data-driven continuing education and skill enhancement. These intelligent systems, such as HealthStream’s jane® AI, Elsevier’s ClinicalKey Nursing, and Wolters Kluwer’s Lippincott Learning, use machine learning and real-time data analytics to deliver up-to-date clinical guidelines, assess clinical judgment, identify competency gaps, and tailor learning experiences based on individual needs and clinical roles, optimizing nurse onboarding and continuous professional development. AI algorithms analyze learning behaviors, clinical decision-making patterns, and performance metrics to recommend targeted training programs, guide nurses toward advanced certifications, and support career progression by identifying emerging trends in healthcare [71, 72, 73].

4.1 Ethical considerations

In nursing practice, ethical concerns primarily involve patient autonomy, beneficence, and justice [74]. AI must be used to support patient-centered care by ensuring transparency in decision-making and respecting patient rights. Informed consent becomes particularly complex with AI-driven recommendations, as patients may not fully understand how AI models generate insights [75]. Nurses play a critical role in educating patients about AI’s role in their care and ensuring they retain the right to accept or reject AI-supported interventions. Furthermore, bias in AI algorithms is another key ethical issue that can contribute to disparities in healthcare. AI models trained on non-representative datasets may produce recommendations that disproportionately favor certain populations, exacerbating health inequities [7]. Nursing professionals must advocate for the development of AI tools that are transparent, evidence-based, and regularly evaluated for fairness. Additionally, the principle of nonmaleficence—ensuring AI does not cause harm—requires rigorous oversight to prevent AI-generated errors, misdiagnoses, or inappropriate treatment recommendations. Finally, the integration of robotics in nursing presents challenges, including high costs, technological limitations, and acceptance by healthcare providers and patients. Training and responsible implementation to address these challenges are required. Preserving human-centered care should be considered as a part of the integration.

In nursing education, significant ethical considerations include transparency, bias, and the impact on critical thinking. One primary ethical concern is algorithmic bias, as AI-driven educational tools may reflect disparities if trained on non-representative datasets, potentially disadvantaging certain student groups [76]. Another ethical issue is the erosion of critical thinking, where over-reliance on AI-assisted decision-making tools may limit students’ ability to independently analyze clinical situations and develop sound judgment [77]. Additionally, data privacy and student autonomy are crucial, as AI-based platforms collect and analyze vast amounts of student data, raising questions about consent, data ownership, and confidentiality.

4.2 Legal considerations

AI in nursing operates within an evolving legal landscape that must balance innovation with accountability and patient rights. One major concern is liability for AI-driven clinical decisions. If an AI system provides incorrect recommendations, determining responsibility among AI developers, healthcare providers, and institutions becomes complex [43]. Nurses remain accountable for exercising professional judgment when interpreting AI-generated insights and ensuring they align with best practices and patient needs. Another legal consideration involves data privacy and security. Particularly given AI’s integration with EHRs and remote monitoring tools. Unauthorized access, AI system vulnerabilities, and data breaches pose significant risks that healthcare organizations must mitigate through robust cybersecurity measures. All AI systems handling sensitive patient information must comply with legal frameworks such as the U.S. Health Insurance Portability and Accountability Act (HIPAA) or the General Data Protection Regulation (GDPR) in Europe to protect patient confidentiality [7, 78].

4.3 Social considerations

The social impact of AI in nursing extends beyond clinical practice, influencing workforce dynamics, patient trust, and healthcare accessibility. While AI streamlines administrative tasks and enhances efficiency, concerns about job displacement and the dehumanization of patient care persist. AI automation should be implemented as a supportive tool rather than replacing human expertise, ensuring nurses remain central to care delivery. Public trust in AI-driven healthcare is another crucial factor affecting AI adoption. Patients may be skeptical of AI-generated recommendations, particularly if they feel human providers do not adequately address their concerns [45]. AI also has the potential to bridge healthcare gaps in underserved communities by enabling remote monitoring, telehealth consultations, and AI-assisted diagnostics. However, disparities in AI access, digital literacy, and healthcare infrastructure can create new barriers for certain populations, exacerbating health inequities rather than addressing them [46].

4.4 Technical considerations

The implementation of AI in nursing demands careful attention to challenges in critical technical infrastructure. Healthcare data systems—the foundation of any AI initiative—exist as fragmented repositories with inconsistent formatting, terminology standards, and completeness levels. Infrastructure limitations further complicate AI deployment. Many healthcare facilities operate with aging technology ecosystems, insufficient bandwidth, and unreliable network connectivity that fail to meet the demands of computationally intensive AI applications. The heterogeneous device landscape—from stationary workstations to tablets and smartphones—creates additional complexity for developers attempting to deliver consistent AI functionality across various hardware platforms. Geographic and institutional disparities in technological resources introduce significant interoperability and equity concerns. This technological divide threatens to concentrate AI benefits in already advantaged settings while excluding vulnerable populations from potential care improvements. Also, the human-technology interface presents equally significant technical challenges. Nurses already experience considerable cognitive load during their shifts, making them particularly susceptible to alert fatigue, information overload, and workflow disruptions. Technical solutions must account for these human factors through thoughtful interface design, contextual awareness, and adaptive systems that provide relevant information without overwhelming users. Successful AI integration in nursing ultimately depends on human-centered technology design principles prioritizing accessibility, transparency, and clinical relevance. Development frameworks must incorporate extensive nursing input from conception through implementation, ensuring that technical considerations align with practical nursing workflows rather than forcing clinical practice to adapt to technological constraints.