Jul 2026· Florence Nightingale journal of nursing· Vol 34, pp. 1-8· 0 citations· 10 references
Medicine
TL;DR
The analysis shows that chatbots and AI systems improve monitoring, therapeutic adherence, and access to healthcare services, and AI-based nutritional models showed improved anthropometric and biochemical indicators and a reduction in nutritional risk in patients with chronic kidney disease.
Abstract
Aim: To evaluate the effectiveness of artificial intelligence (AI)-based chatbots in virtual nursing care at home, comparing them with traditional care, in terms of therapeutic adherence and improvement of clinical outcomes. Methods: A systematic review was conducted on March 7, 2025, according to the the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The databases consulted were PubMed (Medline) and the Cochrane Library. Studies published since 2020 in Italian or English were included. After the screening process, six studies dealing directly or indirectly with the use of AI in home nursing consultation were included. Results: The analysis shows that chatbots and AI systems improve monitoring, therapeutic adherence, and access to healthcare services. Specifically, AI-based nutritional models showed improved anthropometric and biochemical indicators and a reduction in nutritional risk in patients with chronic kidney disease. Smart home systems have improved the timeliness of clinical event detection and the nurse’s decision-making role; medication support robots have reduced nursing workload and improved therapeutic safety. Critical issues related to training, professional acceptance, and technological infrastructure have emerged, limiting the full integration of AI into home care. Conclusion: This review highlights a growing interest in integrating AI into home nursing care, with promising preliminary results. The study fills a gap in the literature by identifying current trends, benefits, and limitations and highlights the need for further clinical research to validate the effectiveness of AI-based nursing chatbots.
Artificial intelligence (AI) is supporting clinical decision-making processes in healthcare systems, personalizing patient care, and optimizing workflows. The aim of this narrative review is to examine the role of AI technologies in supporting physicians, nurses and physiotherapists, as well as to evaluate the barriers, ethical issues, and educational transformations associated with integration. Articles related to the topic were selected from Google Scholar, PubMed, and Scopus search databases using the keywords “Artificial intelligence, healthcare team, healthcare services, informatics,” without any restrictions on publication year. Findings show that clinical decision support systems increase diagnostic accuracy and, through personalized treatment planning, enhance treatment efficacy. In nursing, AI-supported monitoring systems improve patient safety while reducing administrative burden; in physiotherapy, robotic devices, wearable sensors, and machine learning-based movement analysis support rehabilitation. AI-based health education requires new competencies such as health, data, and human literacy. Key barriers include lack of algorithmic transparency, data privacy concerns, bias, and legal uncertainty regarding accountability. In conclusion, AI functions as “augmented intelligence” that complements rather than replaces healthcare professionals. Effective integration requires transparent infrastructures, clear legal boundaries, workforce training, and human-centered practices. Healthcare teams utilizing these AI-supported systems can maximize patient health outcomes.
Ramazan Demirer· İstanbul Gelişim Üniversites...· 0 citations
The need for communication is common to all nurses during patient interaction in different medical scenarios, such as palliative care and end of life care. It is important to note that many nurses and nursing students perceive their level of training as inadequate to deal with difficult conversations. The two tools – virtual reality and artificial intelligence – provide practical help in achieving this.
To synthesize empirical evidence on artificial intelligence (AI)- and virtual reality (VR)-based communication skills training among nurses and nursing students and examine its implications for palliative care education.
Systematic review conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement.
Original studies written in English and published from database inception until March 2026 were identified. Studies that used AI- and/or VR-based communication training for nurses or nursing students using randomized controlled, quasi-experimental, mixed-methods, cross-sectional, or qualitative study designs were eligible for inclusion in this review. Thirteen studies were included in this review. Quality of the methodology was appraised using the Mixed Methods Appraisal Tool (MMAT).
Communication training through VR led to increased confidence in end-of-life communication, improved postmortem care knowledge, increased motivation and high acceptability. A 360 VR communication workshop was found to be feasible and scalable. Multiuser VR simulation reached the same communication outcome as live simulation, and VR-based communication training was more effective than video-based training for basic nurse-patient communication.
AI- and VR-based communication training represents an innovative tool for nursing education, specifically in the field of palliative and end-of-life care. However, further randomized controlled trials with objective performance measurements and long-term follow-ups are needed due to the heavy reliance on subjective self-reports in the existing literature.
Introduction Telemedicine and artificial intelligence (AI) are reshaping healthcare delivery, yet their integration into routine family practice remains inconsistent. This systematic review and meta-analysis aimed to identify barriers and enablers influencing the adoption of telemedicine and AI in primary care and to quantify their effects on clinical and operational outcomes. Methods This review followed PRISMA guidelines and was registered with PROSPERO (CRD420251029675). Six databases—PubMed, Scopus, Web of Science, Cochrane Library, CINAHL, and IEEE Xplore—were searched from inception to March 15, 2025. Eligible studies were randomized controlled trials examining telemedicine or AI implementation in family practice or primary care settings. Data extraction covered study characteristics, intervention type, outcomes, and reported barriers and enablers. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Narrative synthesis, thematic analysis, and random-effects meta-analysis were conducted where appropriate. Results Thirteen randomized controlled trials involving more than 29,000 participants were included. Telemedicine interventions showed favorable study-level effects on chronic disease management, access to care, and patient satisfaction, although the pooled clinical effect across ten trials did not reach statistical significance. AI applications significantly improved diagnostic accuracy and clinical decision-making, with a pooled log odds ratio of 0.73 (95% CI: 0.46–1.00). Key enablers included clinician engagement, reliable digital infrastructure, leadership support, training, and workflow integration. Major barriers included technological interoperability, clinician skepticism, patient digital literacy limitations, privacy concerns, and uncertain reimbursement or governance frameworks. Discussion Telemedicine and AI have promising roles in strengthening family practice, particularly for access, chronic disease management, diagnostic support, and decision-making. Sustainable implementation requires attention to technological, organizational, regulatory, and human factors, alongside targeted training, policy support, and integration into routine primary care workflows.
M. Albarqi· Frontiers in Digital Health· 0 citations
A theoretically grounded synthesis of research gaps and implementation priorities for AI development aligned with nursing clinical judgement is provided, identifying research gaps and implementation priorities for AI development aligned with nursing clinical judgement.
J. Alves, Ana Rita Ribeiro de Azevedo, R. Encarnação et al.· Journal of Advanced Nursing· 0 citations
A meta-analysis examined the effectiveness of artificial intelligence (AI)-based educational interventions on learning outcomes in nursing students and found that AI-based interventions significantly improved knowledge acquisition.
Orkun Erkayıran· Bandırma Onyedi Eylül Üniver...· 0 citations
BACKGROUND
Artificial intelligence (AI) is reshaping clinical decision support systems (CDSSs). In acute and critical care, nurses provide continuous surveillance, recognise deterioration, coordinate escalation and translate protocols into bedside action. AI-CDSS may be particularly relevant when they support rather than replace clinical judgement.
AIM
To examine whether nurse-used AI-CDSS improve patient-important outcomes in acute and critical care contexts and summarise effects on care processes and nurse-reported outcomes.
STUDY DESIGN
Following PRISMA 2020 and a preregistered protocol, we searched eight databases and major trial registries for English-language studies from 1 January 2010 to 1 January 2026. Searches were conducted on 1 January 2026. We included randomised, quasi-experimental and adjusted cohort studies in which registered nurses or nursing teams were primary users of AI-CDSS generating patient-specific predictions or recommendations. Mortality was pooled using a random effects model; other outcomes were synthesised narratively.
RESULTS
Seven studies involving about 75 000 patients were included. Most evidence came from acute wards, intensive care units, sepsis, deterioration and delirium-prevention contexts, with additional home and palliative care evidence. Three mortality studies were pooled. Nurse-facing AI-CDSS were associated with lower hospital mortality (RR 0.68, 95% CI 0.53-0.87; I2 = 24%), although the prediction interval included possible no effect. Length of stay and protocol adherence generally improved when tools were embedded in nursing workflows. Nurse-reported outcomes were sparse.
CONCLUSION
Nurse-facing AI-CDSS may strengthen acute and critical care nursing by improving surveillance, escalation and protocol delivery for patients at risk of deterioration. Evidence is promising but limited by small study numbers, heterogeneous interventions and sparse nurse-reported outcomes. Critical care implementation should prioritise nurse-centred design, alert burden, equity, safety monitoring and rigorous evaluation before scale-up.
RELEVANCE TO CLINICAL PRACTICE
Nurse-used AI-CDSS show potential to improve patient outcomes and care processes, but evidence remains limited and context dependent.
W. Almagharbeh, S. Alkubati, A. A. Alasmari et al.· Nursing Critical Care· 0 citations
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