Improving Acute Care Surgery with Artificial Intelligence: A Practical Review

Artificial intelligence (AI) and machine learning are poised to transform trauma care across the entire continuum, from prehospital triage to postoperative critical care. Trauma systems are uniquely suited for AI integration due to the time-sensitive, high-volume, and data-rich nature of care delivery. In this narrative review, we describe current and emerging AI applications across the trauma care spectrum, including triage, acute resuscitation, operative decision-making, intensive care, and detection of complications. We also examine AI's potential in nontraditional care environments, including prehospital, rural, and military settings, where resource constraints and variability in provider expertise pose significant challenges. Across multiple domains, AI models outperform conventional approaches in predicting injury severity, identifying patients in need of intervention, and detecting complications. Specific tools include AI-powered triage support, resuscitation sequencing systems, real-time imaging interpretation, and outcome prediction applications. Despite this promise, many AI applications remain investigational, and widespread adoption will require validation, transparency, and alignment with ethical and regulatory standards. Thoughtful implementation of AI in trauma care has the potential to enhance decision-making, improve patient outcomes, and address disparities in access to high-quality trauma care.

Duke Scholars

Author Krista Lynn Haines Trauma, Acute, and Critical Care Surgery