{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T01:08:17Z","timestamp":1780448897736,"version":"3.54.1"},"reference-count":100,"publisher":"Georg Thieme Verlag KG","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Clin Colon Rectal Surg"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1055\/a-2769-0955","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T00:11:04Z","timestamp":1767053464000},"page":"178-184","source":"Crossref","is-referenced-by-count":1,"title":["Improving Acute Care Surgery with Artificial Intelligence: A Practical Review"],"prefix":"10.1055","volume":"39","author":[{"given":"William M.","family":"Rice","sequence":"additional","affiliation":[{"name":"Division of Trauma, Acute, and Critical Care Surgery, Department of Surgery, Duke University Medical Center, Durham, North Carolina, United States"},{"name":"Department of Surgery, Eastern Virginia Medical School at Old Dominion University, Norfolk, Virginia, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rachel S.","family":"Morris","sequence":"additional","affiliation":[{"name":"Division of Trauma and Acute Care Surgery, Department of Surgery, Medical College of Wisconsin, Milwaukee, Wisconsin, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Krista L.","family":"Haines","sequence":"additional","affiliation":[{"name":"Division of Trauma, Acute, and Critical Care Surgery, Department of Surgery, Duke University Medical Center, Durham, North Carolina, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"194","published-online":{"date-parts":[[2025,12,29]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","first-page":"S8","DOI":"10.1097\/00005373-199909001-00004","article-title":"A historical perspective of trauma system development in the United States","volume":"47","author":"R J Mullins","year":"1999","journal-title":"J Trauma"},{"issue":"06","key":"ref2","first-page":"1638","article-title":"Advanced trauma life support, 8th 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