ReviewJournal of hepatology2024
Artificial intelligence-assisted digital pathology for non-alcoholic steatohepatitis: current status and future directions.
Review in Journal of hepatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Who cites it
50 citing papers in PubMed.
- Digital quantitation of bridging fibrosis and septa reveals changes in natural history and treatment not seen with conventional histology.Liver international : official journal of the International Association for the Study of the Liver · 2024Trial
- The liver in diabetes: current state and future perspectives.Diabetologia · 2026Review
- Review
- Liver Biopsy in Metabolic Dysfunction-Associated Steatohepatitis Clinical Trials: Potential Pitfalls and Solutions.Liver international : official journal of the International Association for the Study of the Liver · 2026Review
- AI digital pathology as a key tool providing in-depth understanding of the progression and regression of MASH and fibrosis in male mouse models.Nature communications · 2026Article
- Metabolic dysfunction-associated steatotic liver disease: pathogenesis and novel treatment options.Molecular biomedicine · 2026Review
- Background Liver Fibrosis as a Microenvironmental Risk Factor for Intrahepatic Recurrence in Colorectal Liver Metastases.Annals of surgical oncology · 2026Article
- Metabolic dysfunction-associated steatotic liver disease and steatohepatitis-associated hepatocarcinoma preclinical models.Nature reviews. Gastroenterology & hepatology · 2026Review
- AI-assisted fibrosis scoring in MASH: Exploring pathologist decision-making with an SHG-based AI digital pathology tool.JHEP reports : innovation in hepatology · 2026Article
- Decoding organ fibrosis: mechanistic insights and emerging therapeutic strategies.Signal transduction and targeted therapy · 2026Review
- Association between LDL-R (exon 8 C.1171 G > A) polymorphisms and response to antiviral therapy in hepatitis C virus infection.Scientific reports · 2026Article
- AI portal tract detection and characterisation for a regional analysis of steatosis and inflammation in MASLD, MASH and AIH.Journal of clinical pathology · 2026Observational
- Research Progress on the Application of Radiomics and Deep Learning in Liver Fibrosis.Journal of imaging · 2026Review
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.Gut and liver · 2026Review
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- TRIM47: molecular characteristics, disease-related mechanisms, and clinical translational value.Frontiers in immunology · 2026Review
- Biomarkers for early identification of metabolic dysfunction-associated steatotic liver disease (MASLD): a narrative review.Archives of medical science : AMS · 2026Article
- Ethical challenges and governance of artificial intelligence in hepatocellular carcinoma management : Wan DL et al. Ethical considerations in AI for hepatocellular carcinoma.Medical oncology (Northwood, London, England) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
Abstract
The worldwide prevalence of non-alcoholic steatohepatitis (NASH) is increasing, causing a significant medical burden, but no approved therapeutics are currently available. NASH drug development requires histological analysis of liver biopsies by expert pathologists for trial enrolment and efficacy assessment, which can be hindered by multiple issues including sample heterogeneity, inter-reader and intra-reader variability, and ordinal scoring systems. Consequently, there is a high unmet need for accurate, reproducible, quantitative, and automated methods to assist pathologists with histological analysis to improve the precision around treatment and efficacy assessment. Digital pathology (DP) workflows in combination with artificial intelligence (AI) have been established in other areas of medicine and are being actively investigated in NASH to assist pathologists in the evaluation and scoring of NASH histology. DP/AI models can be used to automatically detect, localise, quantify, and score histological parameters and have the potential to reduce the impact of scoring variability in NASH clinical trials. This narrative review provides an overview of DP/AI tools in development for NASH, highlights key regulatory considerations, and discusses how these advances may impact the future of NASH clinical management and drug development. This should be a high priority in the NASH field, particularly to improve the development of safe and effective therapeutics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.