ReviewThe British journal of radiology2026
Artificial intelligence prognostication of liver disease using imaging.
Review in The British journal of radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
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0 citing papers in PubMed.
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
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