Acute pancreatitis is a common inflammatory digestive disease with an unpredictable clinical course, ranging from self-limited forms to severe forms, associated with complications and increased mortality. Early identification of patients at risk of severe disease is particularly important from a surgical perspective, as it has a significant impact on subsequent management. Traditional severity scores, such as APACHE (Acute Physiology And Chronic Health Evaluation) II and BISAP (Bedside Index for Severity in Acute Pancreatitis), remain widely used, but their rigid structure and delayed applicability may limit initial risk assessment. In this review we highlight the evolving role of artificial intelligence in predicting the severity of acute pancreatitis and supporting clinical decision-making, with a focus on surgical management. Recent advances show that data-driven models could improve early risk assessment compared to traditional methods. Although their potential clinical benefits are becoming increasingly clear, real-world implementation remains limited. Initial results are encouraging, but important questions regarding reliability, safety, and integration into clinical practice still need to be addressed.
Dumitrascu et al. (2026) studied this question.