Interventional AI-Human Collaboration for Steatotic Liver Disease Screening

Steatotic liver disease (SLD) is one of the most prevalent chronic liver diseases worldwide, affecting nearly 30% of the global population and projected to exceed 55% by 2040. Timely identification and management of intermediate- and high-risk SLD patients are essential, yet early detection remains challenging because current diagnostic modalities, such as biopsy, ultrasonography, and serum indices, are invasive, insensitive, operator-dependent, or difficult to scale. In contrast, non-contrast CT is widely available in routine care and offers substantial potential for opportunistic SLD screening, although this value has not been fully utilized. Our previously developed MAOSS model accurately identifies intermediate- and high-risk individuals, with MAOSS score≥1.6 combined with Fibro Score ≥1.7, demonstrating high sensitivity and specificity in our large-scale retrospective study. However, despite these promising retrospective findings, the model has not undergone prospective interventional validation, and it remains unclear whether an AI-guided workflow can truly enhance clinical risk stratification, diagnostic yield, and downstream management in real-world SLD populations. Therefore, a prospective intervention study is needed to determine whether MAOSS-guided identification and recall of at-risk individuals can meaningfully improve fibrosis detection and optimize clinical care pathways for SLD.