ReviewInternational journal of emergency medicine2026
Artificial intelligence in emergency medicine: from algorithmic performance to real-world clinical integration - understanding the implementation gap.
Review in International journal of emergency medicine, 2026. 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
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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
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEmergency departments (EDs) operate under time pressure, diagnostic uncertainty, and cognitive overload. Artificial intelligence (AI)-driven clinical decision support systems (CDSS) promise to enhance diagnostic accuracy, risk stratification, and workflow efficiency. However, translation from algorithmic performance to bedside integration remains inconsistent. MAIN BODY: This narrative review synthesizes current evidence on AI-based CDSS in emergency medicine, focusing on diagnostic performance, clinical impact, and implementation barriers. Literature published between 2015 to December 2025 was reviewed across PubMed, Scopus, and Embase. Applications include AI-assisted triage, sepsis prediction, stroke identification, ECG interpretation, imaging analysis, and ED operational forecasting. Many systems demonstrate strong retrospective performance (AUROC 0.85-0.95) and earlier identification of high-risk patients compared with conventional scoring tools. However, prospective multicenter validation demonstrating mortality reduction or sustained length-of-stay improvement remains limited. Performance degradation across institutions, algorithmic bias, data governance challenges, workflow disruption, alert fatigue, medico-legal ambiguity, and lack of explainability are persistent barriers to adoption. Evidence suggests AI is most effective when deployed as decision augmentation rather than autonomous replacement.
conclusionsAI-CDSS in emergency medicine demonstrates promising diagnostic capability but limited real-world outcome validation. Future development must prioritize prospective implementation trials, transparent model reporting, clinician engagement, and equitable performance across diverse populations to ensure safe and effective integration into emergency care systems.
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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.