Advances in the clinical application of machine learning in acute pancreatitis: a review - Zhaowang Tan; Gaoxiang Li; Yueliang Zheng; Qian Li; Wenwei Cai; Jianfeng Tu; Senjun Jin (Frontiers in Medicine)
Traditional disease prediction models and scoring systems for acute pancreatitis (AP) are often inadequate in providing concise, reliable, and effective predictions regarding disease progression and prognosis. As a novel interdisciplinary field within artificial intelligence (AI), machine learning (ML) is increasingly being applied to various aspects of AP, including severity assessment, complications, recurrence rates, organ dysfunction, and the timing of surgical intervention. This review focuses on recent advancements in the application of ML models in the context of AP.