Artificial Intelligence for Predictive Diagnostics, Prognosis, and Decision Support in MASLD, Hepatocellular Carcinoma, and Digital Pathology

Abstract

Artificial intelligence (AI) has fundamentally transformed the landscape of hepatology by enhancing disease diagnosis, risk stratification, and decision support. In metabolic dysfunction–associated steatotic liver disease (MASLD), AI has been integrated into large-scale consortia such as NIMBLE, LITMUS, TARGET-NASH, and SteatoSITE to improve diagnostic accuracy and patient management. These consortia utilize AI to derive and validate non-invasive biomarkers in fibrosis staging. AI-based models also enhance the detection of hepatocyte ballooning and metabolic dysfunction–associated steatohepatitis, minimizing interobserver variability and improving clinical trial enrollment criteria. Additionally, AI applications differentiate MASLD from alcohol-associated liver disease using gut microbiome and metabolic profiling.

In hepatocellular carcinoma (HCC), AI has improved risk stratification, diagnosis, and prognostication. AI-driven models based on liver stiffness and clinical parameters can risk stratify patients for HCC development. Enhanced imaging techniques, radiomics, and histopathology powered by AI improve the accuracy of detecting indeterminate liver nodules and predicting microvascular invasion. AI also improves treatment response prediction for therapies such as transarterial chemoembolization (TACE) and immune checkpoint inhibitors and thereby individualizes therapeutic strategies and improves survival outcomes.

In digital pathology, AI has redefined fibrosis staging, donor liver steatosis assessment, and disease diagnosis. FibroNest™ and qFibrosis are two exceptional AI platforms that utilize imaging techniques for the purposes of both standardizing histological assessments, as well as increasing diagnostic precision. The field of MASLD, HCC, and digital pathology is advancing towards precision medicine.

FibroNest™ and qFibrosis are two exceptional AI platforms that utilize imaging techniques for the purposes of both standardizing histological assessments, as well as increasing diagnostic precision.