Prospective multicenter studies and standardized tele-ICU structures, as well as clinically interpretable AI models suitable

The gap

Evidence profile

Sourced from the recommendations and future work and conclusions of the source papers, classified as general, spanning 7 journals.

Supporting evidence — 7 representative gaps

• Leveraging Smart Health Technologies in Critical Care Nursing to Enhance Palliative Care Quality for End-of-Life Patients (2026) · International Journal of Drug Delivery Technology · doi

  • Standardize the "SHT-Palliative Bundle" as a mandatory protocol for all patients identified as "end-of-life" in ICUs to ensure proactive symptom management. • Encourage nurses to use digital communication to maintain patient-family bonds, platforms especially when physical visitation is restricted. Incorporate "Palliative Informatics" the undergraduate and postgraduate nursing curricula at Sohag University to prepare future nurses for AI-integrated environments. into • • Provide ongoing training sessions for ICU staff on interpreting AI pain analytics and managing Illness Conversations" via digital "Serious prompts. Invest in secure, non-invasive wearable sensors and AI decision-support systems across all critical care departments at Sohag University Hospitals. • • Establish clear institutional guidelines regarding the privacy of digital health data for EOL patients. • Conduct future studies using a control group to the effectiveness of SHT further validate compared to traditional care. Investigate the impact of these technologies on reducing burnout and moral distress among critical care nurses over longer periods (12 months). • References: l Adedokun, S. A. (2024). The integration of artificial intelligence in nursing research: A framework for enhanced data management and evidence-based practice. LAUTECH Faculty of Nursing Sciences' Postgraduate Seminar, College of Health Sciences, LAUTECH, Nigeria. DOI:10.13140/RG.2.2.29746.26569 Ogbomoso, l Afenigus, A. D. (2024). Evaluating pain in non-verbal critical care patients: A narrative review of the critical care pain observation tool and its clinical applications. Frontiers in Pain Research, 5, Article 1481085. doi: 10.3389/fpain.2024.1481085 l Ahmad, S. F., Han, H., Alam, M. M., Rehmat, M. K., Irshad, M., Arraño-Muñoz, M., & Ariza-Montes, A. (2023). Impact of artificial intelligence on human loss in decision making, in education. Humanities and Social Sciences Communications, 10(1), 311. https://doi.org/10.1057/s41599-023-01787- 8 laziness

  generalrecommendations
  Keywords: care pain critical patients nurses digital nursing sciences palliative management postgraduate sohag university future decision
• Artificial Intelligence in Obstetrics and Gynecology Nursing: Clinical, Educational, and Ethical Perspectives (2026) · Cureus · doi

  Based on the evidence synthesized in this review, several priority areas for future research and implementation can be identified. First, multicenter validation studies are needed to strengthen the reliability and generalizability of AI-based maternal risk prediction tools, particularly for conditions such as preeclampsia, gestational diabetes, and preterm birth, which were commonly identified in the included studies. Second, there is a need for implementation research evaluating how AI-enabled clinical decision support systems and monitoring technologies can be effectively integrated into nursing workflows, including their impact on clinical decision-making, workload, and patient outcomes. Third, context- sensitive studies in low- and middle-income settings should be prioritized, given the limited representation of such contexts in the current evidence base and the potential relevance of AI in addressing delays in maternal care. In addition, nurse-led innovation and participatory design approaches should be encouraged to ensure that AI tools are aligned with frontline clinical needs and nursing practice realities. Policymakers and healthcare institutions should establish regulatory frameworks that promote ethical deployment, protect patient privacy, and ensure equitable access to digital health technologies. Finally, integrating AI literacy into undergraduate, postgraduate, and continuing professional development programs is essential to prepare OBG nurses for technology-enhanced practice. Structured training initiatives may enhance confidence, support critical appraisal of AI tools, and facilitate their responsible and sustainable integration into clinical care.

  generalrecommendations
  Keywords: clinical tools based evidence implementation identified maternal decision support technologies nursing patient care ensure practice
• Clinical accuracy and applications of the pediatric assessment triangle in emergency care: a narrative review (2026) · International Journal of Emergency Medicine · doi

  Pediatric EDs are likely to continue integrating the Pedi- atric Assessment Triangle (PAT) with electronic health records (EHR) and artificial intelligence (AI) to improve triage. The PAT provides a rapid and simple method of assessment, allowing clinicians to evaluate a child’s appearance, breathing, and circulation in less than a min- ute [50]. A study of more than 300,000 children found that abnormal PAT findings were associated with a higher likelihood of hospital admission (OR 2.21), ICU admis- sion (OR 4.44), and longer PED stays (OR 1.78) [21]. Another study demonstrated that nurses applying PAT could clearly identify children with conditions such as respiratory distress, shock, or neurologic/metabolic com- promise [20]. AI and machine learning show promise in reshaping pediatric triage. Reviews indicate that these tools can predict critical illness and hospital admission more pre- cisely than traditional methods by analyzing EHR data [51]. More recently, a generative AI model was shown to predict pediatric Emergency Severity Index (ESI) scores, suggesting that AI may serve as a valuable triage aid [52]. Another pediatric study developed an AI model that clas- sified PED patients into urgent, non-urgent, and emer- gency categories, achieving an F1 score of 90%, thereby reducing misclassification between urgent and non- urgent cases [53]. If PAT observations are directly entered into the EHR system, AI may help identify children at high risk for deterioration. Future studies should evaluate how this integration could improve triage speed, diagnostic accuracy, and clinical outcomes in pediatric emergency settings. In the future, PAT may also be supported by wearable sensors that provide continuous physiologic monitoring in pediatric EDs. With wearable devices, clinicians may be able to continuously monitor children’s vital signs and detect clinical changes earlier. Research has dem- onstrated that wearable biosensors can accurately track heart rate and respiratory rate in emergency settings. In one study, a wearable biosensor device showed strong agreement with standard measurements—heart rate (r = 0.87) and respiratory rate (r = 0.75)—during continuous Rath et al. International Journal of Emergency Medicine (2026) 19:103 Page 8 of 11 Fig. 2 Five-level triage model adapted from Shanghai Children’s study monitoring, supporting its potential application in PEDs [54]. Another study in Bangladesh used a wireless wear- able device linked to a smartphone to monitor children with sepsis in a pediatric ICU. The device captured over 99% of vital sign data and showed strong agreement with clinician-measured values. Importantly, these data, when combined with machine learning, helped

  generalfuture work
  Keywords: pediatric children triage emergency urgent wearable rate respiratory model device assessment improve clinicians evaluate hospital
• Artificial intelligence assisted telemedicine, clinical decision support for anesthesia and critical care in intensive care units: a scoping review (2026) · BMC Anesthesiology · doi

  Hence, Future studies should focus on prospective multicenter studies and standardized tele-ICU structures, as well as clinically interpretable AI models suitable for safe integration into clinical critical care. Challenges related to interoperability, explainability, workflow ARTICLE IN PRESSARTICLE IN PRESS ACCEPTED MANUSCRIPT integration, and clinician trust remain to be addressed to further limit broader implementation. The evidence, however, is currently heterogeneous, mostly comprising observational research and preliminary implementation studies, and has not been fully validated with prospective clinical trials.

  generalconclusions
  Keywords: prospective integration clinical article press implementation hence future focus multicenter standardized tele structures well clinically
• Diagnostic Accuracy of Medical Imaging–Based Artificial Intelligence for Osteonecrosis of the Femoral Head: Systematic Review and Meta-Analysis (2026) · Journal of Medical Internet Research · doi

  Translating AI-assisted ONFH diagnosis from research into clinical practice requires advancements in several key areas. First, prospective, multicenter clinical implementation studies are needed to embed AI systems into real-world imag- ing workflows, evaluate their performance stability across different equipment and acquisition parameters, and delineate their scope and boundaries of applicability. Second, mul- timodal models integrating x-ray, MRI, and clinical text data should be developed to enhance diagnostic robustness, with large language models used to generate structured reports whose interpretive logic can be verified by clinicians. Third, building on QUADAS-2 and incorporating AI-spe- cific reporting standards such as Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–Artificial Intelligence (TRIPOD-AI) and Check- list for Artificial Intelligence in Medical Imaging (CLAIM) [50,51], standardized and publicly available ONFH imaging J Med Internet Res 2026 | vol. 28 | e95648 | p. 14 (page number not for citation purposes) JOURNAL OF MEDICAL INTERNET RESEARCH

  generalfuture work
  Keywords: clinical onfh diagnosis models reporting artificial intelligence medical imaging internet translating assisted practice requires advancements
• Evaluating Telemedical Supervision for Critical Anesthesia Scenarios: Randomized Controlled Simulation Study. (2026) · JMIR Medical Informatics · doi

  In this randomized controlled study, we evaluated a telemedical supervision system for anesthesia residents managing critical scenarios. The study demonstrated performance similar to traditional on-site supervision, with similar completion rates of necessary SOP measures. Despite increased temporal demands reported by participants, the system was well received overall, highlighting its potential to address staffing shortages and resource limitations. in anesthesiology and beyond. First, These findings carry important implications for the future of telemedicine the comparable adherence to SOPs in a high-stakes scenario suggests that TSV could be a viable option in situations where on-site senior staff is unavailable. This may be particularly relevant for rural hospitals, understaffed departments, or nighttime operations, where immediate in-person support may not always be feasible. Second, the ability of the telemedical system to integrate real-time device and patient data using the IEEE 11073 SDC interoperability standard lays the groundwork for scalable, interoperable, and future-ready supervision models. As digital infrastructure in hospitals continues to evolve, systems like this could be enhanced with decision support tools, AI-based alerts, or predictive analytics to further assist both supervisors and residents. Third, the study highlights the need for careful integration of new technology into clinical workflows. The increased temporal workload and the mixed feelings regarding remote observation underline the importance of proper training, user adaptation, and human-centered design. Future implementations should include training sessions as part of clinical education, possibly even integrating telemedical scenarios into simulation-based curricula. Moving forward, real-world evaluations are essential to validate these findings in clinical practice. Pilot deployments in hospitals, supported by fallback mechanisms and mixed-modality supervision models, could test how the system performs under actual operating conditions. Additionally, future studies could expand the scope to include different critical events, surgical disciplines, or varying levels of clinician experience. Longitudinal research could also examine how repeated exposure and increasing familiarity affect both performance and user perceptions over time. Finally, beyond anesthesiology, the architecture of such an interoperable telemedical system could be adapted for or combined with existing proprietary solutions for use in other high-acuity areas such as emergency departments, intensive care units, or interhospital consultations. With the global trend toward digital transformation in health care, establishing robust, interoperable TSV systems has the potential to reshape clinical collaboration and training, ultimately improving patient care and operational efficiency.

  generalfuture work
  Keywords: system telemedical supervision future clinical hospitals interoperable training care residents critical scenarios performance similar site
• Artificial Intelligence-Enabled Smart Healthcare (2026) · Future Internet · doi

  As Guest Editors, we believe that the contributions brought together in this Spe- cial Issue make a timely and significant contribution to the evolving area of AI-enabled healthcare. Rather than converging on a single technological pathway, they collectively illustrate the breadth and complexity of a field increasingly defined by the integration of predictive, perceptual, conversational, embedded, multimodal, and human-guided forms of intelligence within clinically relevant healthcare environments. The collection spans real-time fall detection and physiological monitoring, medical-image and lung-sound diag- nostics, maternal risk prediction, neurodegenerative disease management, rehabilitation, mental-health conversational AI, critical-care resource allocation, and telemedicine-enabled post-discharge assessment [1–12]. Their significance stems not only from the technical advances they report, but also from the comprehensive perspective they offer on the require- ments for meaningful adoption, including the following: (a) interoperability and technical resilience, (b) patient engagement, patient-centered implementation and accessibility, (c) clinical validation, (d) human oversight, and (e) privacy, security, and regulatory compli- ance. We hope that this Special Issue will encourage continued interdisciplinary research and dialog at the intersection of artificial intelligence, medicine, digital infrastructure, and health governance. https://doi.org/10.3390/fi18100507 Future Internet 2026, 18, 507 5 of 6 From a broader perspective, this Special Issue should be seen not as a conclusion, but as part of an expanding and still rapidly developing research agenda. The thematic diversity and scientific momentum reflected in these contributions suggest that AI-enabled healthcare is entering a new development phase with an expanding clinical and societal scope. This evolution is characterized by increasing methodological sophistication and maturity, as well as a deeper awareness of the operational conditions required for meaningful adoption. Most recent contributions reinforce this perspective by showing the importance of low-latency edge intelligence for preventive monitoring, accessible web deployment for AI-supported diagnosis, and patient engagement when digital care is extended into the home [10–12]. In this sense, the present collection provides both a timely synthesis of current advances and a strong foundation for future scholarly dialog. It also naturally points toward the value of a second edition, which could further explore the next wave of developments in intelligent, trustworthy, interoperable, and patient-centered healthcare systems. Acknowledgments: The Guest Editors would like to express their sincere gratitude to all contributing authors for their valuable scholarly work and to the reviewers for their thoughtful evaluations and dedication throughout the peer-review process. Particular appreciation is extended to the editorial team of Future Internet for its outstanding professionalism, prompt and thoughtful communication, careful coordination, and continuous support throughout the preparation and publication of this Special Issue. The team’s responsiveness and collegial approach greatly facilitated the editorial process and made this collaboration particularly constructive and rewarding. Conflicts of Interest: The authors declare no conflicts of interest.

  generalfuture work
  Keywords: issue healthcare patient contributions enabled intelligence perspective special future guest editors timely conversational human collection

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