Artificial intelligence (AI) has the potential to revolutionise healthcare delivery, particularly in the domain of emergency medicine. With the rise of telemedicine and virtual care, AI-powered tools could assist in triage, diagnosis and treatment recommendations for patients seeking emergency care remotely. This systematic review aims to synthesise the current state of research on AI applications in virtual emergency care, identify key challenges and opportunities and provide recommendations for future research and implementation.

We will conduct a comprehensive search of multiple electronic databases (PubMed, Web of Science, Embase, CINAHL, MEDLINE, The Cochrane Library, Scopus) from each database’s inception to March 2025. The search will include terms related to AI, machine learning, deep learning, virtual care, telemedicine and emergency medicine. We will include original research articles, conference proceedings and preprints that describe the development, validation or implementation of AI models for virtual emergency care. Two reviewers will independently screen titles and abstracts, review full texts, extract data and assess risk of bias using the PROBAST (Prediction model Risk Of Bias ASsessment Tool) tool for prediction model studies, Cochrane Risk-of-Bias tool for randomised trials for randomised trials and Risk Of Bias In Non-randomised Studies of Interventions for non-randomised studies. Data synthesis will involve a narrative review of included studies, summarising key findings, methodological approaches and implications for practice and research. The results will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

No ethical approval is required for this systematic review as it will use only published data. The findings will be disseminated through publication in a peer-reviewed journal, presentations at relevant conferences and engagement with clinicians, health system leaders, policymakers and researchers. This review will provide a timely and comprehensive overview of the applications of AI in virtual emergency care to inform evidence-based guidelines, policies and practices for leveraging these technologies to enhance access, quality and efficiency of emergency care delivery.

CRD42025648202.

Virtual reality (VR) technology is increasingly being explored as a medium for delivering mindfulness-based interventions. While studies have investigated the feasibility and efficacy of VR-based mindfulness interventions, there has been limited synthesis of user experiences and perceptions across diverse applications, hindering the iterative refinement of these technologies and limiting evidence-based guidance for effective deployment in real-world settings. This systematic review aims to comprehensively identify, appraise and synthesise qualitative research on end-user experiences and perceptions of VR-based mindfulness interventions. Understanding user experiences is critical for translating research findings into practical design improvements and implementation strategies that enhance intervention effectiveness and user adoption.

A systematic search will be conducted in PubMed, Web of Science, Embase, CINAHL, MEDLINE, The Cochrane Library, PsycINFO and Scopus from inception to present. Studies reporting qualitative data on adult participants’ experiences, perceptions, attitudes or opinions related to VR-based mindfulness interventions will be included. Two independent reviewers will screen studies, extract data and assess methodological quality using the Critical Appraisal Skills Programme checklist. Thematic synthesis will be used to analyse the qualitative data. The Grading of Recommendations Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research approach will be applied to assess confidence in the review findings.

Ethical approval is not required as this review will be based on published studies. The findings will be disseminated through peer-reviewed publication and conference presentations.

CRD42024594330.

This study aimed to evaluate the cost-effectiveness of integrating nutritional support into India’s National Tuberculosis Elimination Programme (NTEP) using the MUKTI initiative.

Economic evaluation.

Primary data on the cost of delivering healthcare services, out-of-pocket expenditure and health-related quality of life among patients with tuberculosis (TB) were collected from Dhar district of Madhya Pradesh, India.

Integration of nutritional support (MUKTI initiative) into the NTEP of India.

Routine standard of care in the NTEP of India.

Incremental cost per quality-adjusted life year (QALY) gained.

A mathematical model, combining a Markov model and a compartmental susceptible–infected–recovered model, was used to simulate outcomes for patients with pulmonary TB under NTEP and MUKTI protocols. Primary data collected from 2615 patients with TB, supplemented with estimates from published literature, were used to model progression of disease, treatment outcomes and community transmission dynamics over a 2-year time horizon. Health-related quality of life was assessed using the EuroQol 5-Dimension 5-Level scale. Costs to the health system and out-of-pocket expenditures were included. A multivariable probabilistic sensitivity analysis was undertaken to estimate the effect of joint parameter uncertainty. A scenario analysis explored outcomes without considering community transmission. Results are presented based on health-system and abridged societal perspectives.

Over 2 years, patients in the NTEP plus MUKTI programme had higher life years (1.693 vs 1.622) and QALYs (1.357 vs 1.294) than those in NTEP alone, with increased health system costs (11 538 vs 6807 (US$139 vs US$82)). Incremental cost per life year gained and QALY gained were 67 164 (US$809) and 76 306 (US$919), respectively. At the per capita gross domestic product threshold of 161 500 (US$1946) for India, the MUKTI programme had a 99.9% probability of being cost-effective but exceeded the threshold when excluding community transmission.

The findings highlight the potential benefits of a cost-effective, holistic approach that addresses socio-economic determinants such as nutrition. Reduction in community transmission is the driver of cost-effectiveness of nutritional interventions in patients with TB.

Artificial intelligence (AI) technologies are increasingly being developed and deployed to support clinical decision-making, care delivery and patient monitoring in healthcare. However, the adoption of AI-driven solutions by nurses, who comprise the largest segment of the healthcare workforce and are central to patient care, has been limited to date. Understanding nurses’ perceptions of barriers and facilitators to AI adoption is critical for successful integration of AI in nursing practice. This systematic review aims to identify, appraise and synthesise qualitative evidence on nurses’ perceived barriers and facilitators to adopting AI-driven solutions in their clinical practice.

We will conduct systematic searches across eight electronic databases (PubMed, Web of Science, Embase, CINAHL, MEDLINE, The Cochrane Library, PsycINFO and Scopus) from inception to January 2025, supplemented by hand-searching reference lists and grey literature. Primary qualitative studies and qualitative components of mixed-methods studies exploring licensed/registered nurses’ perceptions of AI adoption in clinical settings will be included. Two independent reviewers will screen studies, extract data using standardised forms and assess methodological quality using the Critical Appraisal Skills Programme checklist. We will employ meta-ethnography to synthesise the qualitative evidence, involving systematic comparison and translation of concepts across studies to develop overarching themes and a theoretical framework. The Grading of Recommendations Assessment, Development and Evaluation Confidence in the Evidence from Reviews of Qualitative Research (GRADE-CERQual) approach will be used to assess confidence in review findings. The protocol follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) guidelines and the Enhancing Transparency in Reporting the Synthesis of Qualitative Research (ENTREQ) statement.

No ethical approval is required as this systematic review will synthesise data from published studies only. The findings will provide valuable insights to inform the development, implementation and evaluation of nurse-oriented strategies for AI integration in healthcare delivery. Results will be disseminated through peer-reviewed publication, conference presentations and stakeholder engagement activities.

CRD42024602808.

This study conducted a comprehensive probabilistic cost-effectiveness analysis comparing robotic exoskeleton therapy to conventional physiotherapy for stroke rehabilitation in Singapore, focusing on three patient groups categorised by their Functional Ambulation Category (FAC) scores.

A probabilistic cost-effectiveness analysis was conducted alongside a non-randomised controlled study. Costs and Quality-Adjusted Life Years (QALYs) for both interventions were calculated and compared over a 6 month period.

The study was carried out at Alexandra Hospital, Jurong Community Hospital and St Luke’s Hospital in Singapore.

Individuals requiring inpatient gait rehabilitation from acute to subacute stages of stroke recovery, with FAC scores of 0–1, were included in the analysis.

The primary outcome measure was QALYs, a composite measure combining the length and quality of life into a single value.

Robotic exoskeleton therapy was found to be cost-effective compared with conventional physiotherapy across all patient groups, with Group 2 (FAC 0) showing the most favourable cost-effectiveness profile (incremental cost-effectiveness ratio (ICER): US$ 28 259.62 per QALY gained). The probabilistic sensitivity analysis demonstrated the robustness of the results, with QALY gains and the cost of the robotic exoskeleton having the largest impact on the ICER.

The findings suggest that robotic exoskeleton therapy is likely to be cost-effective for stroke rehabilitation in Singapore, particularly for patients with severe mobility impairments (FAC 0). The results have important implications for clinical practice, resource allocation and future research in the field of stroke rehabilitation in Singapore.

NCT05659121.

Our study was designed to assess whether paired normal-tumour testing increased access to targeted therapy, clinical trials and influenced cancer screening recommendations given to patients and their families.

Prospective cohort study.

Academic cancer centre in the Pacific Northwest region of the USA.

Patients newly diagnosed between 01 January 2021 and 31 December 2022 with cancers of the oesophagus, gastro-oesophageal junction and stomach (CEGEJS) were included. All other cancer diagnoses such as head and neck, duodenal and lower gastrointestinal tract cancers were excluded.

Paired germline and tumour genetic test within 90 days of new patient visit.

Number of targeted therapies received (or not) when eligible, follow-up treatment data and number of inherited predispositions to cancers identified. No secondary outcome measures.

Of 42 patients, 32 (76.2%) were eligible for at least one targeted therapy. 19 patients received immunotherapy, when 16 had a biomarker predicting immunotherapy benefit, and benefit of immunotherapy was unclear for 3. Another 11 did not have this biomarker, and 6 of them received immunotherapy. Six pathogenic variants were identified in four high-risk genes. By 01 January 2024, 18 patients (42.9%) had died of complications of cancer.

More than 75% of patients who received tumour testing were eligible for a targeted therapy regardless of their stage at diagnosis, emphasising the need to expand access to testing with staging workup to improve survival outcomes. Six families received personalised screening recommendations, thanks to this study.