ReviewFrontiers in medicine2025
Artificial intelligence in hepatocellular carcinoma screening: applications and challenges.
Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic performance of artificial intelligence models in hepatocellular carcinoma: a systematic review and diagnostic meta-analysis.BMC gastroenterology · 2026Pooled it
- Artificial intelligence-powered liquid biopsy in cancer: a paradigm shift in cancer detection and personalized care.Cancer cell international · 2026Review
- Machine learning models for predicting liver cancer: a real-world cohort study in China.Frontiers in cell and developmental biology · 2026Article
- The translational paradox of AI in hepatocellular carcinoma: from algorithmic over-engineering to real-world clinical utility.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) is the predominant histological subtype of primary liver cancer, with a 5-year survival rate of approximately 18%. Early detection of HCC is critical for guiding treatment selection and improving patient survival outcomes. The effectiveness of conventional screening methods is decreased due to their inherent limitations and individual variability. Artificial intelligence (AI) has advanced rapidly in medical practice and has played a significant role in increasing the early detection rates of HCC by replacing manual tasks and accessing hidden information in routinely available clinical data. However, numerous challenges, such as ethical concerns, model instability, and generalizability, must be overcome before their full clinical implementation. This article reviews recent studies that describe AI-based models for the early diagnosis of HCC, focuses on the current applications and persistent challenges of AI in HCC screening and discusses its perspectives. We aim to provide a critical evaluation of the potential of AI for enhancing early HCC detection and improving patient prognosis.
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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.