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Army Medic Performance in Trauma Sonography: The Impact of Artificial Intelligence Assistance in Focused Assessments
C P T Andrea D Hartline1,2, M A J Shay Hartvickson1, C P T Matthew J Perdue1,2
1Department of Emergency Medicine, Carl R. Darnall Army Medical Center, Fort Cavazos, TX 76544, United States.
Introduction:
Noncompressible truncal hemorrhage is a leading cause of preventable death in military prehospital settings, particularly in combat environments where advanced imaging is unavailable. The Focused Assessment with Sonography in Trauma (FAST) exam is critical for diagnosing intra-abdominal bleeding. However, Army medics typically lack formal ultrasound training. This study examines whether artificial intelligence (AI) assistance can enhance medics' proficiency in performing FAST exams, thereby improving the speed and accuracy of trauma triage in austere conditions.
Materials And Methods:
This is a prospective, randomized controlled trial that involved 60 Army medics who performed 3-view abdominal FAST exams, both with and without AI assistance, using the EchoNous Kosmos device. Investigators randomized participants into 2 groups and evaluated based on time to completion, adequacy of imaging, and confidence in using the device. Two trained investigators assessed adequacy and the participants reported confidence in the device using a 5-point Likert scale. We then analyzed data using the t-test for parametric data, the Wilcoxon rank-sum test, and Cohen's Kappa test for interrater reliability.
Results:
The AI-assisted group completed the FAST exam in an average of 142.57 seconds compared to 143.87 seconds (P = .9) for the non-AI-assisted group, demonstrating no statistically significant difference in time. However, the AI-assisted group demonstrated significantly higher adequacy in the left upper quadrant and pelvic views (P = .008 and P = .004, respectively). Participants reported significantly higher confidence in the AI-assisted group, with a median score of 4.00 versus 2.50 (P = .006). Interrater agreement was moderate to substantial, with Cohen's Kappa values indicating significant reliability.
Conclusions:
AI assistance did not significantly reduce the time required to complete a FAST exam but improved image adequacy and user confidence. These findings suggest that AI tools can enhance the quality of FAST exams conducted by minimally trained medics in combat settings. Further research is needed to explore integrating AI-assisted ultrasound training in military medic curricula to optimize trauma care in austere environments.
