ReviewFrontiers in immunology2022
Artificial intelligence for precision medicine in autoimmune liver disease.
Review in Frontiers in immunology, 2022. 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
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
14 citing papers in PubMed, 23 citations in OpenAlex.
- Article
- Diagnosis of liver fibrosis degree in autoimmune liver disease by magnetic resonance diffusion-weighted imaging.Abdominal radiology (New York) · 2026Article
- Development of Large Language Model Specialized into Microbiome Datasets: an Application of Self-Evaluation and Scoring Comparison with Conventional Natural Language Processing Markers.Journal of microbiology and biotechnology · 2026Article
- Mechanisms of Action and Biomarker Potential of ncRNAs in Hepatocellular Carcinoma and Liver Fibrosis: A Review.Analytical cellular pathology (Amsterdam) · 2026Review
- Artificial Intelligence in Biomedicine: A Systematic Review from Nanomedicine to Neurology and Hepatology.Pharmaceutics · 2025Review
- Rare liver diseases - Etiology, diagnosis and management: A review.Biomolecules & biomedicine · 2025Review
- Radiomics-Based Prognostication in Primary Sclerosing Cholangitis: A Proof-of-Concept Study.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- Gut-liver axis dysregulation and microbial dysbiosis in invasive liver abscess: a narrative review.Frontiers in immunology · 2025Review
- Artificial intelligence in hepatocellular carcinoma screening: applications and challenges.Frontiers in medicine · 2025Review
- The Promise of Explainable AI in Digital Health for Precision Medicine: A Systematic Review.Journal of personalized medicine · 2024Review
- Risk Stratification in Primary Biliary Cholangitis.Journal of clinical medicine · 2023Review
- Consensus statements of the Hellenic Autoimmune Liver Diseases Study Group on the diagnosis and current management of primary biliary cholangitis.Annals of gastroenterologyArticle
- Review
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autoimmune liver diseases (AiLDs) are rare autoimmune conditions of the liver and the biliary tree with unknown etiology and limited treatment options. AiLDs are inherently characterized by a high degree of complexity, which poses great challenges in understanding their etiopathogenesis, developing novel biomarkers and risk-stratification tools, and, eventually, generating new drugs. Artificial intelligence (AI) is considered one of the best candidates to support researchers and clinicians in making sense of biological complexity. In this review, we offer a primer on AI and machine learning for clinicians, and discuss recent available literature on its applications in medicine and more specifically how it can help to tackle major unmet needs in AiLDs.
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.