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

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.