ArticleBMJ open2025
Role of artificial intelligence in virtual emergency care: a protocol for a systematic review.
Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionArtificial 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. METHODS AND ANALYSIS: 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. ETHICS AND DISSEMINATION: 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. PROSPERO REGISTRATION NUMBER: CRD42025648202.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.