Artificial intelligence (AI) is increasingly integrated into emergency general surgery (EGS), where rapid diagnosis, accurate decision-making, and timely intervention are essential for improving patient outcomes. Recent advances in machine learning, deep learning, computer vision, and predictive analytics have enabled AI-assisted systems to support clinicians throughout the perioperative workflow. Current applications include radiologic image interpretation, diagnosis of acute abdominal conditions, surgical workflow recognition, intraoperative anatomical guidance, postoperative complication prediction, and intensive care monitoring. AI technologies may improve diagnostic accuracy, optimize operative planning, enhance surgical safety, and facilitate personalized perioperative management. In minimally invasive surgery, computer vision and real-time data analysis have shown promising results for intraoperative decision support and surgical education. However, important limitations remain, including concerns regarding data quality, algorithm transparency, ethical governance, regulatory approval, and implementation disparities between healthcare systems. In addition, much of the current evidence is derived from retrospective or highly specialized datasets, limiting broad clinical applicability. This narrative review summarizes the current clinical applications of AI in emergency general surgery and discusses emerging technologies, existing challenges, and future perspectives regarding the integration of AI into acute surgical care.
Cosma et al. (Mon,) studied this question.
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