ArticleJHEP reports : innovation in hepatology2026
AI-assisted fibrosis scoring in MASH: Exploring pathologist decision-making with an SHG-based AI digital pathology tool.
Article in JHEP reports : innovation in hepatology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Weakly supervised deep learning distinguishes alcohol-associated from metabolic dysfunction-associated steatohepatitis on H&E whole-slide images.Journal of pathology informatics · 2026Article
- How to apply artificial intelligence (AI) to facilitate and enhance MASH trials.Hepatology international · 2026Review
- Machine Learning-Based Radiopatho-Clinical Model Integrating Ultrasound Radiomics and Kleiner Score for Prognosis Prediction in NAFLD-Related Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND &
aimsIntra- and inter-reader variability complicates liver biopsy scoring for metabolic dysfunction-associated steatohepatitis (MASH). AI-powered digital pathology (AI-DP) is emerging as a tool to assist pathologists, with the promise of improving pathologist concordance. This study evaluates how second harmonic generation (SHG)-based AI-DP influences pathologist decision-making for fibrosis scoring and provides guidance for integrating SHG-based AI assistance into pathology workflows.
methodsFour pathologists reviewed 120 MASH cases with and without the assistance tool. We assessed changes in concordance, scoring, and pathologist perceptions via surveys and detailed case reviews. A qualitative analysis was performed on 22 selected cases (18% of all MASH cases reviewed), including cases with reduced or increased inter-pathologist variability and pathologist-AI discrepancies.
resultsPathologists reported the assistance tool as significantly helpful in 57% of cases, prompted conscious score changes in a further 12%, and not useful in 30%. Notably, score shifts between cases with and without assistance occurred even when the assistance tool was deemed unhelpful, indicating a subtle influence on scoring. The assistance tool reduced discrepancies in 65% (33/51) of previously discrepant cases, but increased discrepancies in 27% (15/66 with 1-score discrepancy and 3/66 with >1-score discrepancy) of cases initially without discrepancies. This reflects inherent pathologist variability and differing thresholds and confidence in challenging or borderline cases. Analysis of concordance changes with the assistance tool provided insights into pathologists' fibrosis staging considerations.
conclusionSHG-based AI-DP tools are promising adjuncts for fibrosis scoring in MASH. Our findings provide practical insights and recommendations for effective AI integration and highlight areas for future AI development. Collaborative human-AI approaches will be key to advancing precision histopathology and improving clinical trial outcomes in MASH. IMPACT AND IMPLICATIONS: AI-powered digital pathology (AI-DP) as a pathologist's assistance tool can be utilized to improve pathologist concordance. However, pathologist judgement remains critical. In this study, we evaluated how pathologists used the assistance tool and how the platform impacted their decision-making. The findings are significant for pathologists and clinical researchers as they provide guidance for the well-informed use of SHG-based AI-DP as a tool to aid pathologists in fibrosis scoring in metabolic dysfunction-associated steatohepatitis. The insights gained can further inform policymakers about the integration of AI technologies in clinical trials or pathology workflows in clinical practice, promoting standardized practices.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.