Predictive and operational artificial intelligence in emergency radiology for early risk stratification in acute care settings: a scoping review.

introductionEmergency departments face increasing overcrowding, resulting in diagnostic delays, higher mortality, and rising healthcare costs. Early risk stratification is essential to optimise clinical decision-making. Although medical imaging plays a central role in initial assessment, workload pressures and operational constraints have driven the development of artificial intelligence (AI) tools to support triage and prioritisation in emergency radiology.

objectiveTo map and synthesise evidence on the applications, performance, and limitations of AI in emergency radiology for early risk stratification in acute care. MATERIALS AND

methodsA scoping review was conducted in accordance with PRISMA-ScR guidelines and Joanna Briggs Institute recommendations. The protocol was developed a priori and registered in the Open Science Framework. A systematic search of PubMed, Scopus, and Web of Science identified studies published between 2015 and 2025 that evaluated AI applications in medical imaging in acute care settings. Evidence was synthesised using conceptual mapping and thematic analysis.

resultsSeventy-one studies were included. Computed tomography was the most frequently evaluated modality, followed by radiography and ultrasound. Major domains included neurovascular disease, acute cardiopulmonary conditions, trauma, COVID-19, and acute aortic syndromes. Studies reported high diagnostic performance and potential workflow benefits; however, evidence was predominantly retrospective and internally validated, with limited prospective evaluation, external validation, or evidence of improved patient-level outcomes.

conclusionsAI applications in emergency radiology show promising diagnostic and workflow-support capabilities. However, current evidence largely reflects early-stage development, with limited prospective validation, scarce real-world impact assessment, and insufficient evaluation of clinically meaningful outcomes. KEY POINTS: Question How clinically mature are AI applications designed to support early risk stratification and prioritisation in time-sensitive emergency radiology workflows? Findings Most studies report high diagnostic performance in time-sensitive conditions, yet evidence remains predominantly retrospective with limited external validation or prospective real-world implementation. Clinical relevance AI may support earlier identification and prioritisation of critical imaging findings, but routine adoption requires prospective external validation and evidence of meaningful effects on clinical decisions and patient outcomes.