ReviewJournal of clinical medicine2024
AI in Hepatology: Revolutionizing the Diagnosis and Management of Liver Disease.
Review in Journal of clinical medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 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
15 citing papers in PubMed.
- Article
- Risk Assessment and Prediction of Hepatocellular Carcinoma in Noncirrhotic Metabolic Dysfunction-Associated Steatotic Liver Disease.International journal of molecular sciences · 2026Review
- Early Prediction of Hepatic Decompensation in Cirrhosis Using Optimised XGBoost Models at the Initial Outpatient Hepatology Visit.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Harnessing artificial intelligence for the assessment of liver fibrosis and steatosisWorld journal of gastroenterology · 2026Review
- Biomarkers for early identification of metabolic dysfunction-associated steatotic liver disease (MASLD): a narrative review.Archives of medical science : AMS · 2026Article
- Advances in transarterial chemoembolization for hepatocellular carcinoma: Integration with systemic therapies and emerging treatment strategies.Cancer pathogenesis and therapy · 2026Review
- Diagnosis, clinical assessment, and staging of hepatocellular carcinoma: a Brazilian multidisciplinary consensus.Arquivos brasileiros de cirurgia digestiva : ABCD = Brazilian archives of digestive surgery · 2026Article
- Machine learning-based prediction of treatment response in comorbid hepatitis C patients receiving DAA therapy: a real-world study from Pakistan.Frontiers in public health · 2026Article
- [Research progress and future prospects for artificial intelligence in the diagnosis and treatment of fatty liver disease].Zhonghua gan zang bing za zhi = Zhonghua ganzangbing zazhi = Chinese journal of hepatology · 2025Review
- Artificial intelligence in hepatopathy diagnosis and treatment: Big data analytics, deep learning, and clinical prediction models.World journal of gastroenterology · 2025Review
- Review
- The Kidney in the Shadow of Cirrhosis: A Critical Review of Renal Failure.Biomedicines · 2025Review
- AI in steatohepatitis diagnostics: precision beyond the microscope.Annals of medicine and surgery (2012) · 2025Article
- Liver biopsy in the modern era: from traditional techniques to artificial intelligence and multi-omics integration.Frontiers in medicine · 2025Review
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
The integration of artificial intelligence (AI) into hepatology is revolutionizing the diagnosis and management of liver diseases amidst a rising global burden of conditions like metabolic-associated steatotic liver disease (MASLD). AI harnesses vast datasets and complex algorithms to enhance clinical decision making and patient outcomes. AI's applications in hepatology span a variety of conditions, including autoimmune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, MASLD, hepatitis B, and hepatocellular carcinoma. It enables early detection, predicts disease progression, and supports more precise treatment strategies. Despite its transformative potential, challenges remain, including data integration, algorithm transparency, and computational demands. This review examines the current state of AI in hepatology, exploring its applications, limitations, and the opportunities it presents to enhance liver health and care delivery.
Indexed as
Identifiers
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.