Non-invasive diagnosis of metabolic dysfunction-associated steatotic liver disease: Current status, challenges, and future directions.

Journal: Diabetes, obesity & metabolism
Published Date: Dec 4, 2025

Abstract

Metabolic dysfunction-associated steatotic liver disease (MASLD) is the most prevalent liver disease globally and a significant public health challenge posing serious threats to human health. Although a liver biopsy is considered the gold standard for diagnosing MASLD, its invasiveness, high cost, and associated risks limit its clinical applicability. With the rapid advancements in artificial intelligence and imaging technologies, non-invasive biomarkers and machine learning techniques are increasingly being used in the diagnosis of MASLD. This article systematically reviews the latest developments in non-invasive diagnostic technologies for MASLD, focusing on four major areas: serological, imaging, multi-omics, and AI-driven diagnostic models. This study aims to provide evidence-based insights for precise clinical diagnosis, ultimately facilitating early warning, dynamic monitoring, and individualized management of MASLD.

Authors

  • Yuqing Ma
  • Changzhi Zhang
    Department of Internal Medicine III, Yiwu Hospital of Traditional Chinese Medicine, Yiwu, Zhejiang, China.
  • Meng Tian
    Electronic Information School, Wuhan University, Wuhan 430072, China.
  • Jie Hu
    Corteva Agriscience, Farming Solutions and Digital, Indianapolis, IN, United States.
  • Miaojuan Wang
    Department of General Practice, The First Affiliated Hospital of Zhejiang Chinese Medical University, Zhejiang Provincial Hospital of Traditional Chinese Medicine, Hangzhou, China.
  • Shan Liu
    Department of Radiology, General Hospital of Ningxia Medical University, Yinchuan, China.

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