ReviewDiagnostics (Basel, Switzerland)2024
The Use of Artificial Intelligence in the Liver Histopathology Field: A Systematic Review.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 20 citations in OpenAlex.
- AI-Driven Approaches for the Detection, Classification, and Surveillance of Viral Pathogens: Current Advances, Challenges, and Future Directions.Pathogens (Basel, Switzerland) · 2026Review
- Emerging Serological Biomarkers for Autoimmune Hepatitis.Journal of clinical medicine · 2026Review
- High performance deep-learning model for the diagnosis of auto-immune hepatitis based on histological whole slide images.Virchows Archiv : an international journal of pathology · 2026Article
- Artificial intelligence in small tissue biopsies: diagnostic applications, histochemical integration, and methodological challenges in surgical pathology.Histochemistry and cell biology · 2026Review
- Artificial intelligence‑based quantitative analysis of hepatic fibrosis in carbon tetrachloride-induced mouse model of metabolic dysfunction-associated steatohepatitis.Toxicological research · 2026Article
- Artificial Intelligence Applications in the Diagnosis, Treatment, and Prognosis of Hepatocellular Carcinoma.Gut and liver · 2026Review
- Neutrophil and macrophage zonation in liver disease: from spatiotemporal dynamics to advanced computational analysis.Frontiers in immunology · 2026Review
- Whole-slide deep-learning quantification of fibrosis and pathologist-defined proliferative/neoplastic regions in a thioacetamide-induced rat liver model.Frontiers in artificial intelligence · 2026Article
- Navigating the Labyrinth of Hepatocellular Carcinoma: Leveraging AI/ML for Precision Oncology.Oncology research · 2026Review
- Review
- Recent advance in early oral lesion diagnosis: the application of artificial intelligence-assisted endoscopy.Frontiers in oncology · 2025Review
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
- Hepatic Steatosis Analysis in Metabolic Dysfunction-Associated Steatotic Liver Disease Based on Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2024Article
- Joint Expedition: Exploring Clinical Medical Imaging and Artificial Intelligence as a Team Integration.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Digital pathology (DP) has begun to play a key role in the evaluation of liver specimens. Recent studies have shown that a workflow that combines DP and artificial intelligence (AI) applied to histopathology has potential value in supporting the diagnosis, treatment evaluation, and prognosis prediction of liver diseases. Here, we provide a systematic review of the use of this workflow in the field of hepatology. Based on the PRISMA 2020 criteria, a search of the PubMed, SCOPUS, and Embase electronic databases was conducted, applying inclusion/exclusion filters. The articles were evaluated by two independent reviewers, who extracted the specifications and objectives of each study, the AI tools used, and the results obtained. From the 266 initial records identified, 25 eligible studies were selected, mainly conducted on human liver tissues. Most of the studies were performed using whole-slide imaging systems for imaging acquisition and applying different machine learning and deep learning methods for image pre-processing, segmentation, feature extractions, and classification. Of note, most of the studies selected demonstrated good performance as classifiers of liver histological images compared to pathologist annotations. Promising results to date bode well for the not-too-distant inclusion of these techniques in clinical practice.
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What OpenQuestion 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 OpenQuestion graph.