Artificial Intelligence prognostication of liver disease using imaging

Accurate prognostic tools in patients with chronic liver disease (CLD) have the potential to improve clinical outcomes and reduce health care costs. Imaging studies of CLD patients analysed using artificial intelligence (AI) algorithms for segmentation, detection and classification tasks have the potential to inform and improve prognostic models for liver-related outcomes. In this narrative review, we provide an overview of the strengths, weaknesses and approaches to inclusion of AI in prognostic models that use ultrasound (US), computed tomography (CT) and magnetic resonance imaging (MRI). We then use a prognostic endpoint-based approach to examine AI-based US, CT and MRI prognostic models in chronic liver disease (CLD). We highlight how AI has been applied to extract imaging features or build predictive models directly, and assess the limitations that currently hinder clinical translation. We also outline key challenges specific to prognostication in CLD and propose directions for future research.