Artificial Intelligence–Assisted Ultrasound in Austere Trauma Environments: Implications for Role 1 Military Medicine

Abstract

Pararescue jumpers are United States Air Force medical tactical operators who provide advanced trauma and prolonged casualty care in austere, high-risk environments. Trauma represents the predominant clinical scenario within their operational mission set, and care is frequently delivered at or near the point of injury when evacuation to definitive treatment is delayed or unavailable. Despite extensive medical training, care at the most forward level, referred to as Role 1, remains fundamentally limited by the absence of diagnostic imaging. Clinical assessment relies primarily on physical examination and mechanism of injury, which demonstrate limited sensitivity for occult hemorrhage, thoracic injury, and other life-threatening conditions. This diagnostic uncertainty directly affects triage and intervention decisions and carries operational consequences, particularly when evacuation is delayed. Ultrasound improves detection of occult injury and informs intervention and triage decisions and is well suited for austere environments because of its portability, lack of ionizing radiation, and minimal infrastructure requirements. Focused examination protocols enable detection of hemoperitoneum, pericardial effusion, hemothorax, and pneumothorax and influence clinical decision making in hospital and prehospital settings. However, ultrasound adoption at Role 1 remains limited because of operator dependence, challenges with skill sustainment among low-frequency users, competing training demands, cognitive load during trauma care, and environmental constraints. Artificial Intelligence (AI)-assisted ultrasound provides real-time acquisition guidance and interpretive support through machine learning algorithms that recognize anatomic landmarks and assess image quality. Studies involving novice and minimally trained users demonstrate improved image acquisition success with AI guidance compared with unguided scanning. In addition to diagnostic applications, AI-assisted ultrasound may support image-guided procedures within the existing scope of practice, including vascular access and other focused interventions performed under austere conditions. This commentary examines the operational context of Role 1 care and explores how AI-assisted ultrasound may augment diagnostic and procedural capability at the point of injury. Existing evidence suggests that this technology offers a potential pathway to reducing diagnostic uncertainty although aligning with the constraints of forward medical practice. Prospective evaluation in operationally relevant environments and integration into training and doctrine will be essential to determine its impact on survivability and operational effectiveness.

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