ArticleNature medicine2024
AI-based automation of enrollment criteria and endpoint assessment in clinical trials in liver diseases.
Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03449446 (A Phase 2, Randomized, Double-Blind, Placebo-Controlled Study Evaluating the Safety and Efficacy of Selonsertib, GS-0976, GS-9674, and Combinations in Subjects With Bridging), which is not on this map. Cited by 27 papers.
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.
A Phase 2, Randomized, Double-Blind, Placebo-Controlled Study Evaluating the Safety and Efficacy of Selonsertib, GS-0976, GS-9674, and Combinations in Subjects With Bridging (F3) Fibrosis or Compensated Cirrhosis (F4) Due to Nonalcoholic Steatohepatitis (NASH)
Who cites it
27 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
- MAFLD/MASLD: Past, Present, and Future.Journal of gastroenterology and hepatology · 2026Review
- Risk assessment in metabolic dysfunction-associated steatotic liver disease and metabolic dysfunction and alcohol-related liver disease: current approaches and future directions.Clinical and molecular hepatology · 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
- AI in fatty liver diseases: insights from recent research trends.Hepatology international · 2026Article
- Decoding organ fibrosis: mechanistic insights and emerging therapeutic strategies.Signal transduction and targeted therapy · 2026Review
- Label Noise in Pathological Segmentation Is Overlooked, Leading to Potential Overestimation of Artificial Intelligence.Cancer science · 2026Article
- Histological analysis of age-related degeneration in human female and male knee cartilage and meniscus.Osteoarthritis and cartilage open · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Non-Invasive Tests for the Detection of MASLD: Biomarkers and Imaging for Staging Steatosis, MASH, and Fibrosis.International journal of general medicine · 2026Review
- AI redefines fibrosis patterns in steatotic liver disease: Opportunities and challenges.Hepatology forum · 2026Article
- Panomics in metabolic dysfunction-associated steatotic liver disease: unravelling the drivers of disease heterogeneity.Clinical and molecular hepatology · 2026Review
- Application of artificial intelligence in hepatology.Frontiers in digital health · 2026Review
- Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: Current status and future prospects.World journal of gastroenterology · 2025Review
- [Diagnosis and treatment of liver diseases in the era of artificial intelligence].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Review
- Development and validation of an artificial intelligence system for triple-negative breast cancer identification and prognosis prediction: a multicentre retrospective study.EClinicalMedicine · 2025Article
- Endoplasmic reticulum stress at the forefront of fatty liver diseases and cancer.Pharmacological reviews · 2025Review
- Constructing biomimetic microenvironments for liver regeneration.Journal of nanobiotechnology · 2025Review
Corrections and comments
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Authors and funding
31 authors.
Funding
Abstract
Clinical trials in metabolic dysfunction-associated steatohepatitis (MASH, formerly known as nonalcoholic steatohepatitis) require histologic scoring for assessment of inclusion criteria and endpoints. However, variability in interpretation has impacted clinical trial outcomes. We developed an artificial intelligence-based measurement (AIM) tool for scoring MASH histology (AIM-MASH). AIM-MASH predictions for MASH Clinical Research Network necroinflammation grades and fibrosis stages were reproducible (κ = 1) and aligned with expert pathologist consensus scores (κ = 0.62-0.74). The AIM-MASH versus consensus agreements were comparable to average pathologists for MASH Clinical Research Network scores (82% versus 81%) and fibrosis (97% versus 96%). Continuous scores produced by AIM-MASH for key histological features of MASH correlated with mean pathologist scores and noninvasive biomarkers and strongly predicted progression-free survival in patients with stage 3 (P < 0.0001) and stage 4 (P = 0.03) fibrosis. In a retrospective analysis of the ATLAS trial (NCT03449446), responders receiving study treatment showed a greater continuous change in fibrosis compared with placebo (P = 0.02). Overall, these results suggest that AIM-MASH may assist pathologists in histologic review of MASH clinical trials, reducing inter-rater variability on trial outcomes and offering a more sensitive and reproducible measure of patient responses.
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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.