Intelligent diagnosis and treatment and comprehensive digital health management of metabolic dysfunction-associated fatty liver disease

Yewei JIANG; Yunyi XU; Yuru HE; Wangyu QIAO; Mingyang GOU; Jingqi ZHOU

doi:10.12449/JCH260422

Volume 42 Issue 4

Apr. 2026

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Article Navigation > Journal of Clinical Hepatology > 2026 > 42(4): 923-929

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Intelligent diagnosis and treatment and comprehensive digital health management of metabolic dysfunction-associated fatty liver disease

DOI: 10.12449/JCH260422

Yewei JIANG^{1, 2},
Yunyi XU¹,
Yuru HE^{1, 3},
Wangyu QIAO^{1, 4},
Mingyang GOU⁵,
Jingqi ZHOU^{1
,
,
,}

1.
School of Public Health，Shanghai Jiao Tong University School of Medicine，Shanghai 200025，China
2.
School of Pediatrics，Shanghai Jiao Tong University School of Medicine，Shanghai 200025，China
3.
School of Stomatology，Shanghai Jiao Tong University School of Medicine，Shanghai 200025，China
4.
Renji Clinical Medical College，Shanghai Jiao Tong University School of Medicine，Shanghai 200025，China
5.
School of Biomedical Engineering，Shanghai Jiao Tong University，Shanghai 200240，China

Research funding:

Shanghai Jiao Tong University School of Medicine Undergraduate Innovative Training Program (1723X020);

Shanghai High-level Local University Innovation Team (SSMU-ZDCX1723X020)

More Information

Corresponding author: ZHOU Jingqi， jingqizhou@sjtu.edu.cn （ORCID： 0000-0002-9961-199X）
Received Date: 2025-07-31
Accepted Date: 2025-09-26
Published Date: 2026-04-25

Abstract

Abstract

Metabolic dysfunction-associated fatty liver disease （MAFLD） has become one of the most prevalent chronic liver diseases worldwide， posing a serious challenge to public health. In this context， the integration of artificial intelligence （AI）， especially intelligent diagnosis and treatment and digital health interventions based on machine learning， can break through the limitations of traditional methods， realize efficient screening of multi-dimensional data such as key genes， biomarkers， and biochemical metabolites， and achieve revolutionary breakthroughs in risk prediction， subtype identification， and therapeutic effect assessment for MAFLD. This article systematically reviews the ground-breaking application of machine learning models in driving the innovation of clinical diagnosis and precise risk prediction of MAFLD， conducts a comprehensive comparative analysis of digital health practice cases of MAFLD in China and globally， and deeply analyzes their advantages and limitations in terms of research subjects， interventions， and management team. Studies have shown that the deep integration of digital health and long-term management of MAFLD is becoming the key engine driving the transformation of disease management modes towards an intelligent， individualized， and precise era， but there are various ethical and technical issues that need to be addressed urgently.
- Metabolic Dysfunction-Associated Fatty Liver Disease,
- Machine Learning,
- Artificial Intelligence,
- Digital Health

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References

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Intelligent diagnosis and treatment and comprehensive digital health management of metabolic dysfunction-associated fatty liver disease

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