SynthesisJournal of Crohn's & colitis2025
Artificial intelligence-enabled histology exhibits comparable accuracy to pathologists in assessing histological remission in ulcerative colitis: a systematic review, meta-analysis, and meta-regression.
Synthesis in Journal of Crohn's & colitis, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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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.
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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.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic Prediction Models for Ulcerative Colitis: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Advanced endoscopy and artificial intelligence-enabled vascular healing for ulcerative colitis: promising frontiers or mere mirage?Clinical endoscopy · 2026Review
- Artificial intelligence in inflammatory bowel disease: From current evidence, clinical translation, and the road to precision medicine.Chinese medical journal · 2026Review
- A Scoping Review of Endoscopic Assessment Considering Disease Extent in Ulcerative Colitis: Insights for the Artificial Intelligence Era.Gut and liver · 2026Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- Artificial intelligence in inflammatory bowel disease: bridging innovation, implementation and impact.Nature reviews. Gastroenterology & hepatology · 2026Review
- Input structure-driven instability and convergence in large language model clinical reasoning: a formative study using pediatric residency-level MCQs.Scientific reports · 2026Article
- Multimodal deep learning for inflammatory bowel disease: a new frontier in cellular and molecular biomarker discovery to clinical translation.Journal of biological engineering · 2026Review
- Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026Review
- Recent Advances in Artificial Intelligence for Endoscopic and Multimodal Assessment of Inflammatory Bowel Disease: A Review.International journal of general medicine · 2026Review
- Artificial Intelligence in Advancing Inflammatory Bowel Disease Management: Setting New Standards.Cancers · 2025Review
- Endo-Histo foundational fusion model: a novel artificial intelligence for assessing histologic remission and response to therapy in ulcerative colitis clinical trial.Journal of Crohn's & colitis · 2025Article
- Role of serological biomarkers in evaluating and predicting endoscopic activity in inflammatory bowel disease.World journal of gastroenterology · 2025Article
- Artificial intelligence in advanced endoscopic imaging: transforming optical diagnosis in gastroenterology.Frontiers in medicine · 2025Review
- Rediscovering histology - the application of artificial intelligence in inflammatory bowel disease histologic assessment.Therapeutic advances in gastroenterology · 2025Review
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND
aimsAchieving histological remission is a desirable emerging treatment target in ulcerative colitis (UC), yet its assessment is challenging due to high inter- and intraobserver variability, reliance on experts, and lack of standardization. Artificial intelligence (AI) holds promise in addressing these issues. This systematic review, meta-analysis, and meta-regression evaluated the AI's performance in assessing histological remission and compared it with that of pathologists.
methodsWe searched Medline/PubMed and Scopus databases from inception to September 2024. We included studies on AI models assessing histological activity in UC, with or without comparison to pathologists. Pooled performance metrics were calculated: sensitivity, specificity, positive and negative predictive value (PPV and NPV), observed agreement, and F1 score. A pairwise meta-analysis compared AI and pathologists, while sub-meta-analysis and meta-regression evaluated heterogeneity and factors influencing AI performance.
resultsTwelve studies met the inclusion criteria. AI models exhibited strong performance with a pooled sensitivity of 0.84 (95% CI, 0.80-0.88), specificity 0.87 (0.84-0.91), PPV 0.90 (0.87-0.92), NPV 0.80 (0.71-0.88), observed agreement 0.85 (0.82-0.89), and F1 score 0.85 (0.82-0.89). AI models demonstrated no significant differences with pathologists for specificity, observed agreement, and F1 score, while they were outperformed by pathologists for sensitivity and NPV. AI models for the adult population were linked to reduced heterogeneity and enhanced AI performance at meta-regression.
conclusionsAI shows significant potential for assessing histological remission in UC and performs comparably to pathologists. Future research should focus on standardized, large-scale studies to minimize heterogeneity and support widespread AI implementation in clinical practice.
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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.