Artificial intelligence in nursing: an integrative review of clinical and operational impacts

In the paper

“However, despite these benefits, ethical challenges remain prominent. Key concerns include data privacy risks, algorithmic bias, and the potential erosion of clinical judgment due to overreliance on technology.”

In the paper

“several studies reported that early-warning algorithms facilitated faster clinical responses, thereby improving patient safety and outcomes.”

In the paper

“These efficiencies have led to a measurable reduction in nurse burnout and improved job satisfaction, as nurses can devote more time to direct patient care.”

In the paper

“Thematic analysis revealed three primary themes: Clinical and Therapeutic Advancements through AI Integration, Operational Optimization and Staff Support, and Ethical Implications.”

In the paper

“Lastly, a cross-sectional survey exploring nurses’ perceptions and knowledge of AI met four criteria, as its convenience sampling strategy and relatively small sample size limited generalizability beyond the study's regional context (30).”

What the sources say

• Not checked yetcites [30] Sommer, 2024, Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany, BMC Nurs source

In the paper

“AI integration in nursing practice significantly improved diagnostic accuracy and therapeutic interventions.”

In the paper

“In conclusion, the majority of the included studies demonstrated high methodological rigor, meeting all five MMAT criteria.”

In the paper

“Similarly, an RCT evaluating the application of AI in emergency nursing for COPD patients failed to provide sufficient details on randomization procedures and generalizability, meeting only three criteria (32).”

What the sources say

• Not checked yetcites [32] Hong, 2021, Application of artificial intelligence in emergency nursing of patients with chronic obstructive pulmonary disease, Contrast Media Mol Imaging source

In the paper

“Sampling strategies were generally appropriate for the study designs, with many RCTs employing robust randomization techniques to minimize bias.”

In the paper

“The primary drivers of moderate bias were incomplete adjustment for confounders (particularly baseline health differences, demographic factors, and prior AI exposure), reliance on subjective or self-reported outcome measures, and selective reporting due to absent or unreferenced protocols.”

In the paper

“While selection bias was generally low because most studies had well-defined inclusion criteria, a few relied on convenience sampling, which may limit the generalizability of their findings.”

In the paper

“Most of the included studies demonstrated high methodological quality, with 15 studies meeting all five MMAT criteria.”

In the paper

“Interventions were generally well classified, adherence to intervention protocols was high, and missing data were commonly addressed with appropriate statistical methods.”

In the paper

“Du et al. (41) implemented an AI-based Plan-Do-Check-Action (PDCA) home nursing model for patients with diabetic nephropathy, which led to significant improvements in clinical effectiveness and patient satisfaction over 12 months by enabling dynamic adjustments to care plans based on AI-generated insights.”

What the sources say

• Not checked yetcites [41] Du, 2022, Evaluation of functional magnetic resonance imaging under artificial intelligence algorithm on plan-do-check-action home nursing for patients with diabetic nephropathy, Contrast Media Mol Imaging source

In the paper

“AI-based interventions have been effective in reducing nurse burnout and enhancing staff well-being, thereby supporting nurse retention and overall job satisfaction.”

In the paper

“In the realm of therapeutic interventions, Zhang et al. (29) utilized AI-enhanced MRI in pelvic floor muscle rehabilitation for rectal cancer patients, resulting in improved image clarity and diagnostic precision.”

What the sources say

• Not checked yetcites [29] Zhang, 2022, Evaluation of nursing effects of pelvic floor muscle rehabilitation exercise on gastrointestinal tract rectal cancer patients receiving anus-preserving operation by intelligent algorithm-based magnetic resonance imaging, Contrast Media Mol Imaging source

In the paper

“By synthesizing data from 18 studies, three critical themes emerge: Clinical and Therapeutic Advancements through AI Integration, Operational Optimization and Staff Support, and Ethical and Safety Implications.”

In the paper

“Similarly, Chen et al. (47) implemented an AI-based “Internet + Hospital-to-Home” (H2H) nutritional nursing model for chronic kidney disease (CKD) patients. This model optimized nutritional assessments and monitoring through automated data analysis and personalized care plans, reducing manual assessment time and enhancing resource allocation based on real-time patient needs.”

What the sources say

• Not checked yetcites [47] Chen, 2022, Implementation of hospital-to-home model for nutritional nursing management of patients with chronic kidney disease using artificial intelligence algorithm combined with CT internet +, Contrast Media Mol Imaging source

In the paper

“AI-integrated monitoring systems provide continuous, real-time data, enabling nurses to detect clinical changes early and intervene promptly, thereby improving patient safety and care quality.”

In the paper

“Cho et al. (36) evaluated the Nurse Healing Space app, an AI-based mobile intervention designed to reduce burnout. The app significantly decreased burnout scores, job stress, and stress responses among nurses while increasing user satisfaction through personalized recommendations.”

What the sources say

• Not checked yetcites [36] Cho, 2024, Development of an artificial intelligence-based tailored mobile intervention for nurse burnout: single-arm trial, J Med Internet Res source

In the paper

“Similarly, Xu et al. (46) integrated the Otsu method algorithm into indocyanine green angiography (ICGA) for diagnosing intracranial aneurysms, which significantly improved diagnostic precision, reduced complications, and enhanced patient satisfaction.”

What the sources say

• Not checked yetcites [46] Xu, 2022, Cerebral angiography under artificial intelligence algorithm in the design of nursing cooperation plan for intracranial aneurysm patients in craniotomy clipping, Comput Math Methods Med source

In the paper

“Additionally, there is insufficient exploration of the impact of AI on nurse-patient relationships, particularly concerning empathy and compassionate care.”

In the paper

“AI technologies have optimized nursing workflows, resource allocation, and administrative tasks, thereby enhancing operational efficiency and reducing the burden on nursing staff.”

In the paper

“While these themes highlight significant advancements and benefits, our critical analysis reveals persistent challenges such as data privacy concerns, algorithmic biases, and the potential erosion of clinical judgment.”

In the paper

“Nurses called for clear guidelines and frameworks to govern AI's ethical implementation in healthcare to protect patient information and maintain trust in healthcare systems.”

In the paper

“Most of the existing research focuses on high-income settings, leaving a significant gap in understanding how AI integration affects nursing practices in resource-constrained environments.”

In the paper

“The integration of AI into nursing practice introduces several ethical and safety considerations that must be carefully addressed to ensure responsible and equitable implementation. This theme encompasses concerns related to data privacy, algorithmic bias, overreliance on AI systems, job roles, patient autonomy, and overall patient safety.”

In the paper

“In emergency nursing for COPD patients, Hong et al. (32) found that AI-driven telemedicine platforms facilitated real-time patient monitoring and early intervention, which reduced hospitalization rates and enhanced patients’ quality of life.”

What the sources say

• Not checked yetcites [32] Hong, 2021, Application of artificial intelligence in emergency nursing of patients with chronic obstructive pulmonary disease, Contrast Media Mol Imaging source

In the paper

“AI technologies have demonstrated transformative potential in optimizing nursing workflows, resource allocation, and administrative efficiency.”

In the paper

“Liu et al. (34) evaluated the iThermonitor WT705, an AI-assisted wearable thermometer, in surgical wards. The device identified fevers in 22% of patients compared to 17% with standard mercury thermometers and detected fevers up to 4.35 h earlier, allowing nurses to manage potential infections or complications proactively.”

What the sources say

• Not checked yetcites [34] Liu, 2020, Evaluation of a wearable wireless device with artificial intelligence, iThermonitor WT705, for continuous temperature monitoring for patients in surgical wards: a prospective comparative study, BMJ Open source

In the paper

“AI-driven predictive models and automation tools have streamlined nursing workflows and optimized the allocation of resources, ensuring efficient use of nursing personnel and enhancing patient care delivery.”

In the paper

“Racine et al. (42) assessed AI for pain monitoring in neonatal intensive care units (NICUs). The AI-assisted systems enhanced the accuracy and consistency of pain assessments, enabling more effective pain management strategies for preterm infants.”

What the sources say

• Not checked yetcites [42] Racine, 2024, Health care professionals’ and parents’ perspectives on the use of AI for pain monitoring in the neonatal intensive care unit: multisite qualitative study, JMIR AI source

In the paper

“Seibert et al. (48) highlight barriers such as limited data digitization, privacy and security concerns, and the complexity of nursing tasks that restrict AI to simpler functions.”

What the sources say

• Not checked yetcites [48] Seibert, 2023, Exploring needs and challenges for AI in nursing care-results of an explorative sequential mixed methods study, BMC Digital Health source

In the paper

“Seibert et al. (48) highlight the potential of AI to optimize staff scheduling and skill mix, improve route planning for home care services, and aid in institutional workflow management.”

What the sources say

• Not checked yetcites [48] Seibert, 2023, Exploring needs and challenges for AI in nursing care-results of an explorative sequential mixed methods study, BMC Digital Health source

In the paper

“AI-driven diagnostic tools have demonstrated remarkable improvements in diagnostic accuracy and therapeutic effectiveness across various clinical settings.”

In the paper

“Beyond administrative efficiencies, AI also contributes to clinical safety and operational reliability.”

In the paper

“Data quality issues, ethical considerations, and clinician apprehension toward over-reliance on AI tools are notable barriers.”

In the paper

“AI-based interventions have shown considerable promise in mitigating nurse burnout and enhancing job satisfaction.”

In the paper

“The integration of predictive analytics in managing nosocomial infections—such as through antimicrobial stewardship programs—further underscores AI's role in reducing hospital-acquired complications and enhancing operational efficiency.”

In the paper

“AI, encompassing methodologies such as machine learning, natural language processing, and computer vision, holds the potential to enhance diagnostic accuracy, streamline clinical workflows, and support timely, evidence-based decision-making in multiple areas of care (1–3).”

What the sources say

• Not checked yetcites [1] Topol, 2019, High-performance medicine: the convergence of human and artificial intelligence, Nat Med source
• Not checked yetcites [2] Jiang, 2017, Artificial intelligence in healthcare: past, present and future, Stroke Vasc Neurol source
• Not checked yetcites [3] Esteva, 2019, A guide to deep learning in healthcare, Nat Med source

In the paper

“This review demonstrates that AI integration holds transformative potential for nursing practice by enhancing both clinical outcomes and operational efficiency.”

In the paper

“resulting in timely interventions that reduce complications, shorten hospital stays, and lower readmission rates.”

In the paper

“These advancements facilitate earlier and more accurate identification of medical conditions, enable personalized care strategies, and improve overall patient outcomes.”

In the paper

“Two independent reviewers conducted the screening process to minimize bias, with discrepancies resolved through discussion or consultation with a third reviewer.”

In the paper

“Data extraction was performed independently by two reviewers using a standardized data extraction form.”

In the paper

“This integrative review adheres to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines to ensure comprehensive and transparent reporting (23).”

What the sources say

• Not checked yetcites [23] Page, 2021, Updating guidance for reporting systematic reviews: development of the PRISMA 2020 statement, J Clin Epidemiol source

In the paper

“while the ROBINS-I (Risk Of Bias in Non-randomized Studies of Interventions) tool was utilized to assess the risk of bias specifically in non-randomized studies (26).”

What the sources say

• Not checked yetcites [26] Sterne, 2016, ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions, Br Med J source

In the paper

“To minimize publication bias, an additional search for grey literature was also conducted using sources such as conference proceedings, dissertations, and preprint servers.”

In the paper

“Beyond direct patient care, AI can also help manage administrative tasks and staffing logistics, ensuring that resources are deployed efficiently to maintain safe workloads and reduce burnout (18, 19).”

What the sources say

• Not checked yetcites [18] Rožman, 2023, Artificial-intelligence-supported reduction of employees’ workload to increase the company’s performance in today’s VUCA environment, Sustainability source
• Not checked yetcites [19] Pavuluri, 2024, Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics, BMJ Health Care Inform source

In the paper

“The Mixed Methods Appraisal Tool (MMAT) was employed to evaluate the quality of diverse study designs (25)”

What the sources say

• Not checked yetcites [25] Thomas, 2008, Methods for the thematic synthesis of qualitative research in systematic reviews, BMC Med Res Methodol source

In the paper

“A comprehensive literature search was conducted across multiple electronic databases to capture relevant studies published up to November 2024.”

In the paper

“However, it is crucial to recognize that these advancements are contingent upon the availability of high-quality data and the integration of AI systems within existing healthcare infrastructures.”

In the paper

“Zhang et al. (29) utilized AI-enhanced MRI in pelvic floor muscle rehabilitation for rectal cancer patients, resulting in improved image clarity and diagnostic precision.”

What the sources say

• Not checked yetcites Zhang et al. (29) Zhang, 2022, Evaluation of nursing effects of pelvic floor muscle rehabilitation exercise on gastrointestinal tract rectal cancer patients receiving anus-preserving operation by intelligent algorithm-based magnetic resonance imaging, Contrast Media Mol Imaging source

In the paper

“This finding is echoed by Epelde (53), who reported that AI-powered predictive analytics not only optimize staff scheduling but also enhance resource utilization by forecasting bed occupancy and patient flow, thereby reducing operational bottlenecks and improving patient outcomes.”

What the sources say

• Not checked yetcites Epelde (53) Epelde, 2024, Revolutionizing patient safety: the economic and clinical impact of artificial intelligence in hospitals, Hospitals source

In the paper

“Sommer et al. (30) found that despite limited AI knowledge among nurses, the majority view AI positively, recognizing its potential to alleviate workload pressures and improve nursing efficiency.”

What the sources say

• Not checked yetcites Sommer et al. (30) Sommer, 2024, Nurses’ perceptions, experience and knowledge regarding artificial intelligence: results from a cross-sectional online survey in Germany, BMC Nurs source

In the paper

“These advancements collectively highlight AI's capacity to enhance both diagnostic and therapeutic dimensions of nursing care, leading to improved patient outcomes and more personalized care strategies.”

In the paper

“This overreliance can lead to a decrease in the ability to identify nuanced patient needs that require human intuition and empathy, thereby impacting the quality of patient care.”

In the paper

“Armoundas et al. (50), who reported that AI-enhanced cardiac imaging tools, such as echocardiography and cardiac MRI, automated key diagnostic tasks and democratized cardiac expertise, thereby reducing disparities in resource-limited settings.”

What the sources say

• Not checked yetcites Armoundas et al. (50) Armoundas, 2024, Use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the american heart association, Circulation source

In the paper

“In our review, Xu et al. (46) showed how AI-assisted imaging optimized perioperative care, reducing surgical complications and improving postoperative outcomes.”

What the sources say

• Not checked yetcites Xu et al. (46) Xu, 2022, Cerebral angiography under artificial intelligence algorithm in the design of nursing cooperation plan for intracranial aneurysm patients in craniotomy clipping, Comput Math Methods Med source

In the paper

“These advancements enable nurses to integrate high-quality imaging data into patient care plans, enhancing the timeliness and accuracy of diagnoses.”

In the paper

“The exclusion of non-English publications introduces potential language bias, potentially omitting relevant studies conducted in non-English-speaking regions, which may limit the comprehensiveness and generalizability of the findings.”

In the paper

“AI-driven tools enhance patient care quality, optimize resource allocation, and support nursing staff well-being, addressing critical workforce challenges such as burnout and job dissatisfaction.”

In the paper

“By preventing adverse events, improving infection control, and optimizing resource allocation, AI generates cost savings while maintaining high-quality care (53).”

What the sources say

• Not checked yetcites Epelde (53) Epelde, 2024, Revolutionizing patient safety: the economic and clinical impact of artificial intelligence in hospitals, Hospitals source

In the paper

“Automating routine tasks enable nurses to dedicate more time to direct patient care, thereby improving care quality and patient satisfaction.”

In the paper

“This integrative review highlights the transformative impact of AI in nursing practice, revealing significant advancements in clinical diagnostics, therapeutic interventions, and operational efficiencies.”

In the paper

“Adherence to the PRISMA 2020 guidelines and the utilization of SPIDER framework ensured a structured and transparent study selection process, enhancing the reliability and reproducibility of the findings.”

In the paper

“AI holds significant promise in improving nurse well-being and mitigating burnout through targeted mental health interventions, operational efficiency, and workload management.”

In the paper

“However, the integration of AI also presents ethical challenges, including data privacy concerns, algorithmic bias, and the potential erosion of clinical judgment.”

In the paper

“Varnosfaderani et al. (6) emphasized the role of AI in optimizing workflows through automated scheduling and staffing systems, which reduce conflicts and streamline decision-making processes.”

What the sources say

• Not checked yetcites Varnosfaderani et al. (6) Maleki Varnosfaderani, 2024, The role of AI in hospitals and clinics: transforming healthcare in the 21st century, Bioengineering source

In the paper

“These tools not only address stress and anxiety but also offer predictive analytics to identify nurses at risk of mental health issues, enabling early intervention and tailored support.”

What the sources say

• Not checked yetcites Dailah et al. (54)

In the paper

“Bian et al. (37) found that AI-assisted follow-up systems for postoperative care achieved comparable effectiveness to manual methods while drastically reducing time and resource demands.”

What the sources say

• Not checked yetcites Bian et al. (37) Bian, 2020, Artificial intelligence-assisted system in postoperative follow-up of orthopedic patients: exploratory quantitative and qualitative study, J Med Internet Res source

In the paper

“This optimization alleviates the administrative burden on nurses, allowing them to focus more on direct patient care, thereby fostering job satisfaction and professional fulfillment.”

In the paper

“Clinically, AI technologies have the potential to revolutionize patient care by enhancing diagnostic accuracy, personalizing therapeutic interventions, and streamlining clinical workflows, thereby elevating the overall quality of care and patient outcomes.”

In the paper

“Operationally, AI-driven automation of administrative tasks and optimized resource allocation can significantly alleviate the burdens on nursing staff, reducing burnout and enhancing job satisfaction, which is critical for maintaining a sustainable and resilient nursing workforce.”

In the paper

“Additionally, the application of the ROBINS-I tool for risk of bias assessment and the MMAT for quality evaluation provided a robust appraisal of the included studies, ensuring that the synthesized evidence is both valid and reliable.”

In the paper

“By leveraging tools such as predictive analytics and wearable devices, AI empowers nurses to manage their workloads more effectively, promoting resilience and well-being.”