ArticleNature medicine2025
Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis.
Article in Nature medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Cerebrovascular Disease Management: A Comprehensive Review of Risk Prediction, Diagnosis, Therapeutic Optimization, and Clinical Translation.Vascular health and risk management · 2025Pooled it
- AI-enabled clinical trials.Nature reviews bioengineering · 2026Article
- How to apply artificial intelligence (AI) to facilitate and enhance MASH trials.Hepatology international · 2026Review
- Drug Development for MASH-Related Compensated Cirrhosis: Past, Present and Future.Liver international : official journal of the International Association for the Study of the Liver · 2026Review
- Real-World Insights in Designing SteatoStat: An End-to-End Deep Learning Pipeline for Hepatic Steatosis Quantification.Diagnostics (Basel, Switzerland) · 2026Article
- The Asian Pacific Association of the Study of the Liver expert survey on artificial intelligence-assisted reporting of liver histopathology in metabolic dysfunction associated fatty liver disease.Hepatology international · 2026Article
- [Artificial intelligence-assisted diagnostics in the pathology of internal and oncological diseases].Innere Medizin (Heidelberg, Germany) · 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
- "Surrogates without substance?" Questioning the evidence for histologic improvement with HTD1801: Letter to the editor on "HTD1801 demonstrates promising potential for histologic improvements in metabolic dysfunction-associated steatohepatitis in both a preclinical and phase 2 study".Clinical and molecular hepatology · 2026Article
- The Gut-Liver Axis in Metabolic Dysfunction-Associated Steatotic Liver Disease: From Mechanistic Insights to Precision Therapeutics.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- AI portal tract detection and characterisation for a regional analysis of steatosis and inflammation in MASLD, MASH and AIH.Journal of clinical pathology · 2026Observational
- Digital pathology and lipid droplet size as a key determinant of discrepancies between histology and MRI gradings in steatotic liver disease.European radiology · 2026Article
- Applications of Artificial Intelligence and Smart Devices in Metabolic Dysfunction-associated Steatotic Liver Disease.Journal of clinical and translational hepatology · 2026Article
- 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
- AI redefines fibrosis patterns in steatotic liver disease: Opportunities and challenges.Hepatology forum · 2026Article
- Non-Invasive Tests for the Detection of MASLD: Biomarkers and Imaging for Staging Steatosis, MASH, and Fibrosis.International journal of general medicine · 2026Review
- Applications and challenges of utilizing digital pathology and AI-enabled workflows in clinical trials.Journal of pathology informatics · 2026Review
- Obesity Medications Beyond Glucose Control and Weight Reduction: Updates on the Expanding Benefits, Clinical Challenges, and Future Directions.Current hypertension reports · 2025Review
- [Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
Corrections and comments
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Authors and funding
29 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. Manual scoring of liver biopsies, currently the gold standard for clinical trial enrollment and endpoint assessment, suffers from high reader variability. This study represents the most comprehensive multisite analytical and clinical validation of an artificial intelligence (AI)-based pathology system, AI-based measurement of metabolic dysfunction-associated steatohepatitis (AIM-MASH), to assist pathologists in MASH trial histology scoring. AIM-MASH demonstrated high repeatability and reproducibility compared to manual scoring. AIM-MASH-assisted reads by expert MASH pathologists were superior to unassisted reads in accurately assessing inflammation, ballooning, MAS ≥ 4 with ≥1 in each score category and MASH resolution, while maintaining non-inferiority in steatosis and fibrosis assessment. These findings suggest that AIM-MASH could mitigate reader variability, providing a more reliable assessment of therapeutics in MASH clinical trials.
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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.