ReviewJHEP reports : innovation in hepatology2022
Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction.
Review in JHEP reports : innovation in hepatology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers, 3 of them syntheses that pooled it.
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.
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Who cites it
110 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- A systematic review of comparisons of AI and radiologists in the diagnosis of HCC in multiphase CT: implications for practice.Japanese journal of radiology · 2026Pooled it
- Artificial Intelligence for Contrast-Enhanced Ultrasound of the Liver: A Systematic Review.Digestion · 2025Pooled it
- Diagnostic performance of MRI for residual or recurrent hepatocellular carcinoma after locoregional treatment according to contrast agent type: a systematic review and meta‑analysis.Abdominal radiology (New York) · 2024Pooled it
- Interleukin-1β in circulating mononuclear cells predicts steatotic liver disease improvement after weight loss in subjects with obesity and prediabetes or type 2 diabetes.Cardiovascular diabetology · 2025Trial
- Peritumoral ultrasound-driven multimodal AI model for predicting lymphovascular space invasion and prognosis in cervical cancer.Abdominal radiology (New York) · 2026Article
- Response evaluation to systemic therapy in HCC: Current challenges and future perspectives.Hepatology (Baltimore, Md.) · 2026Review
- Machine Learning-Driven Noninvasive Prediction of Liver Fibrosis in Unclassified Liver Disease.MedComm · 2026Article
- Article
- Artificial intelligence prognostication of liver disease using imaging.The British journal of radiology · 2026Review
- Artificial intelligence in hepatocellular carcinoma: imaging-based subtyping and prediction of treatment response and prognosis.Abdominal radiology (New York) · 2026Review
- Clinical applications of immunogenomics in hepatocellular carcinoma.Clinical and molecular hepatology · 2026Review
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Deep learning for fast screening and localization of spinal dural arteriovenous fistulas to enhance clinical workflow.NPJ digital medicine · 2026Article
- Artificial intelligence in metabolic dysfunction-associated steatotic liver disease: Transforming diagnosis and therapeutic approaches.World journal of gastroenterology · 2026Review
- Clinically deployable AI to predict objective response to radiotherapy-intensified immunotherapy in advanced hepatocellular carcinoma.Frontiers in oncology · 2026Article
- Advances in quantitative ultrasound for metabolic dysfunction-associated steatotic liver disease diagnosis.Frontiers in physiology · 2026Review
- An integrated prognostic model incorporating clinical characteristics, multiparametric MRI radiomics, deep learning features, and intratumoral heterogeneity for glioma patients.Frontiers in oncology · 2026Article
- The diagnostic accuracy of imaging methods in preoperative prediction of vessels encapsulating tumor clusters in hepatocellular carcinoma in East Asian populations: a systematic review and meta-analysis.Quantitative imaging in medicine and surgery · 2026Article
- AI-driven diagnostic and prognostic models for metabolic dysfunction-associated steatotic liver disease: insights from clinical, imaging, and multi-omics studies-a scoping review.Frontiers in medicine · 2026Review
- Ultrasound and SWE-based transfer learning for predicting fibrotic NASH.Scientific reports · 2025Article
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
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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.