Use of artificial intelligence to support prehospital traumatic injury care: A scoping review

• Published in: Journal of the American College of Emergency Physicians Open Vol. 5; no. 5; pp. e13251 - n/a
• Main Authors: Toy, Jake, Warren, Jonathan, Wilhelm, Kelsey, Putnam, Brant, Whitfield, Denise, Gausche-Hill, Marianne, Bosson, Nichole, Donaldson, Ross, Schlesinger, Shira, Cheng, Tabitha, Goolsby, Craig
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.10.2024; John Wiley and Sons Inc; Wiley
• Subjects: Age; Artificial intelligence; Blood transfusions; Decision making; Deep learning; Emergency medical care; Emergency Medical Services; Emergency services; Geriatrics; Machine learning; Mass casualty incidents; Natural language processing; Ostomy; Pediatrics; prehospital care; Regression analysis; Review; Trauma; Trauma care; Traumatic brain injury; United States > US; emergency medical services; deep learning; natural language processing; traumatic injury; machine learning; prehospital care; artificial intelligence
• ISSN: 2688-1152; 2688-1152
• DOI: 10.1002/emp2.13251

Artificial intelligence (AI) has transformative potential to support prehospital clinicians, emergency physicians, and trauma surgeons in acute traumatic injury care. This scoping review examines the literature evaluating AI models using prehospital features to support early traumatic injury care. We conducted a systematic search in August 2023 of PubMed, Embase, and Web of Science. Two independent reviewers screened titles/abstracts, with a third reviewer for adjudication, followed by a full-text analysis. We included original research and conference presentations evaluating AI models-machine learning (ML), deep learning (DL), and natural language processing (NLP)-that used prehospital features or features available immediately upon emergency department arrival. Review articles were excluded. The same investigators extracted data and systematically categorized outcomes to ensure consistency and transparency. We calculated kappa for interrater reliability and descriptive statistics. We identified 1050 unique publications, with 49 meeting inclusion criteria after title and abstract review (kappa 0.58) and full-text review. Publications increased annually from 2 in 2007 to 10 in 2022. Geographic analysis revealed a 61% focus on data from the United States. Studies were predominantly retrospective (88%), used local (45%) or national level (41%) data, focused on adults only (59%) or did not specify adults or pediatrics (27%), and 57% encompassed both blunt and penetrating injury mechanisms. The majority used machine learning (88%) alone or in conjunction with DL or NLP, and the top three algorithms used were support vector machine, logistic regression, and random forest. The most common study objectives were to predict the need for critical care and life-saving interventions (29%), assist in triage (22%), and predict survival (20%). A small but growing body of literature described AI models based on prehospital features that may support decisions made by dispatchers, Emergency Medical Services clinicians, and trauma teams in early traumatic injury care.