Medical Devices: Evidence and Research (May 2024)
Artificial Intelligence in Emergency Trauma Care: A Preliminary Scoping Review
Abstract
Christian Angelo I Ventura,1 Edward E Denton,2 Jessica A David3 1Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD USA; Department of Allied Health, Baltimore City Community College, Baltimore, MD, USA; 2Department of Emergency Medicine, University of Arkansas for Medical Sciences, Little Rock, AR USA; Fay W. Boozman College of Public Health, University of Arkansas for Medical Sciences, Little Rock, AR, USA; 3Department of Biochemistry and Microbiology, Rutgers University, New Brunswick, NJ, USACorrespondence: Christian Angelo I Ventura, Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of Public Health Baltimore, Baltimore, MD, USA, Tel +1 732 372-2141, Email [email protected]; [email protected]: This study aimed to analyze the use of generative artificial intelligence in the emergency trauma care setting through a brief scoping review of literature published between 2014 and 2024. An exploration of the NCBI repository was performed using a search string of selected keywords that returned N=87 results; articles that met the inclusion criteria (n=28) were reviewed and analyzed. Heterogeneity sources were explored and identified by a significance threshold of P < 0.10 or an I2 value exceeding 50%. If applicable, articles were categorized within three primary domains: triage, diagnostics, or treatment. Findings suggest that CNNs demonstrate strong diagnostic performance for diverse traumatic injuries, but generalized integration requires expanded prospective multi-center validation. Injury scoring models currently experience calibration gaps in mortality quantification and lesion localization that can undermine clinical utility by permitting false negatives. Triage predictive models now confront transparency, explainability, and healthcare ecosystem integration barriers limiting real-world translation. The most significant literature gap centers on treatment-oriented generative AI applications that provide real-time guidance for urgent trauma interventions rather than just analytical support.Keywords: artificial intelligence, machine-learning, emergency medicine, traumatology
