ReviewDigital health
The current status and future directions of artificial intelligence in the prediction, diagnosis, and treatment of liver diseases.
Review in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Overcoming MDSC-Mediated Immunosuppression in Hepatocellular Carcinoma: From Mechanisms to Novel Immunotherapeutic Approaches.Cancers · 2026Review
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Artificial intelligence applications for managing metabolic dysfunction-associated steatotic liver disease: Current status and future prospects.World journal of gastroenterology · 2025Review
- Artificial Intelligence in Thermal Ablation: Current Applications and Future Directions in Microwave Technologies.Biomimetics (Basel, Switzerland) · 2025Review
- Prevalence of MASLD and Fibrosis Risk in Turkish Adults with Cardiometabolic Risk Factors: A Nationwide Multicenter Study (DAHUDER MASLD Study).Journal of clinical medicine · 2025Article
- Mapping artificial intelligence adoption in hepatology practice and research: challenges and opportunities in MENA region.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early detection, accurate diagnosis, and effective treatment of liver diseases are of paramount importance for improving patient survival rates. However, traditional methods are frequently influenced by subjective factors and technical limitations. With the rapid progress of artificial intelligence (AI) technology, its applications in the medical field, particularly in the prediction, diagnosis, and treatment of liver diseases, have drawn increasing attention. This article offers a comprehensive review of the current applications of AI in hepatology. It elaborates on how AI is utilized to predict the progression of liver diseases, diagnose various liver conditions, and assist in formulating personalized treatment plans. The article emphasizes key advancements, including the application of machine learning and deep learning algorithms. Simultaneously, it addresses the challenges and limitations within this domain. Moreover, the article pinpoints future research directions. It underscores the necessity for large-scale datasets, robust algorithms, and ethical considerations in clinical practice, which is crucial for facilitating the effective integration of AI technology and enhancing the diagnostic and therapeutic capabilities of liver diseases.
Indexed as
Identifiers
What Socratic 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 Socratic graph.