Artificial intelligence (AI), virtual reality (VR), gamification, and telerehabilitation are increasingly incorporated into neurorehabilitation to deliver adaptive, personalized, and remotely accessible interventions for individuals with stroke and other neurological disorders. These technologies aim to address key limitations in conventional rehabilitation by enhancing training intensity, patient engagement, accessibility, and real-time monitoring. This systematic review synthesizes evidence from clinical and simulation-based studies evaluating AI-assisted systems, non-AI gamified platforms, VR/exergames, telerehabilitation models, and simulation-driven architectures across neurological populations. A comprehensive search of PubMed, Scopus, Embase, CINAHL, and Web of Science (2010–2025) identified randomized controlled trials, pilot and quasi-experimental studies, telerehabilitation systems, VR/exergame interventions, AI-based adaptive tools, and computational or model-driven investigations, guided by a revised PICO framework. Data were extracted using a standardized template, with studies categorized by design, population, technological modality, and outcome domain. Risk of bias was assessed using validated tools, and GRADE was applied to stroke-specific clinical outcomes. Twenty-two studies met the inclusion criteria, encompassing both clinical trials and simulation/modeling research. Clinical studies reported improvements in motor function, balance, gait, swallowing, cognition, and psychosocial well-being, often accompanied by high usability and adherence. AI-enabled systems facilitated adaptive difficulty adjustment, automated feedback, and individualized progression, while non-AI platforms demonstrated strong engagement and meaningful functional gains. Simulation studies provided valuable insights into algorithm behavior, sensor-based modeling, and system optimization. Despite promising multi-domain benefits, methodological heterogeneity, limited long-term follow-up, and inconsistent AI transparency remain key challenges, underscoring the need for standardized outcomes, explainable AI, inclusive design, and robust multicenter trials.

Purpose: Road-traffic and trauma emergencies are major causes of preventable mortality in low- and middle-income countries (LMICs). Nurses frequently act as first responders, yet standardized nurse-led triage systems remain inconsistently implemented. This systematic review evaluated the effectiveness of validated nurse-led triage protocols in improving diagnostic accuracy, timeliness, and patient outcomes in trauma and road-traffic injury (RTI) care across low-resource LMIC settings. Methods: Following PRISMA 2020 guidelines, five databases (PubMed, Embase, Scopus, CINAHL, and Web of Science) were searched from January 2010 to October 2025. Eligible studies implemented standardized triage tools such as the South African Triage Scale (SATS) or Rapid Emergency Triage and Treatment System (RETTS) in emergency department or prehospital contexts. Risk of bias was appraised using the QUADAS-2, ROBINS-I, and JBI checklists, and findings were narratively synthesized. Results: Eight studies from Africa, Asia, and the Caribbean (Haiti) met the inclusion criteria, encompassing trauma and road-traffic injury populations in emergency and prehospital settings. Standardized nurse-led triage improved diagnostic accuracy (70– 97%), reduced under-triage (≤ 20%), and shortened waiting times (up to 45%) and on-scene-to-care intervals (~38%). Across settings, structured triage systems enhanced recognition of high-acuity trauma and alignment between triage category and injury severity. The greatest impact occurred when protocols were supported by trained nursing leadership, periodic retraining, and clear decision-support tools. Conclusion: Standardized nurse-led triage is feasible, safe, and effective for strengthening trauma-care systems in resource-limited environments. Embedding structured triage frameworks and empowering nursing leadership can substantially improve diagnostic accuracy, timeliness, and survival outcomes in LMIC emergency care systems. Plain Language Summary: Road-traffic injuries and trauma are major causes of preventable deaths in low- and middle-income countries. In many of these settings, nurses are often the first trained professionals to assess injured patients, either at the roadside or in hospital emergency departments. However, without structured decision tools, delays in identifying the most critical patients can occur. This systematic review examined whether standardized nurse-led triage systems improve the speed and accuracy of trauma care. We reviewed studies from eight countries across Africa and Asia that implemented structured triage tools, such as the South African Triage Scale and the Rapid Emergency Triage and Treatment System. Across diverse settings, nurse-led triage improved diagnostic accuracy (70– 97%) and reduced under-triage rates to 20% or less. Waiting times decreased by up to 45%, and prehospital transport intervals were shortened by approximately 38%. In several studies, accurate triage classification was associated with improved survival and better patient stabilization. Nurses also reported increased confidence and professional autonomy when leading structured triage processes. These findings demonstrate that standardised nurse-led triage is a practical, affordable, and effective strategy for improving trauma care in resource-limited environments. Strengthening nurses training and embedding structured triage protocols within emergency systems can enhance patient safety, optimize resource use, and reduce preventable deaths.