Advances in the Clinical Application of Machine Learning in Acute Pancreatitis: a Review

Overview

Journal Front Med (Lausanne)

Date 2025 Jan 22

PMID 39839637

Authors

Zhaowang Tan

Gaoxiang Li

Yueliang Zheng

Qian Li

Wenwei Cai

Jianfeng Tu

Senjun Jin

Affiliations

Soon will be listed here.

Abstract

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.

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