ReviewBMJ open gastroenterology2025
Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.
Review in BMJ open gastroenterology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Mapping GLP-1 receptor agonist research across the MASLD-MASH-HCC continuum: a bibliometric and translational analysis.Frontiers in medicine · 2026Pooled it
- From black-box prediction to transparent insight: the status quo and paradigm shift of explainable artificial intelligence in hepatocellular carcinoma research.Journal of the Egyptian National Cancer Institute · 2026Review
- Small Extracellular Vesicle-Associated Polymeric Immunoglobulin Receptor in Primary Liver Cancer: From Immunological Mediator to Oncogenic Driver and Biomarker.Cell proliferation · 2026Article
- The evolving landscape of early diagnosis for hepatocellular carcinoma: a bibliometric analysis of global research trends (2016-2026) based on WoSCC database.Translational cancer research · 2026Article
- Automated deep learning for real-time focal liver lesions detection in ultrasound videos a multicenter study.NPJ digital medicine · 2026Article
- Hepatocellular carcinoma surveillance: a health economic evaluation.Clinical and molecular hepatology · 2026Review
- Artificial Intelligence in the Diagnosis and Prognostic Stratification of Hepatocellular Carcinoma: Current Evidence, Clinical Applications, and Future Perspectives.Biomedicines · 2026Review
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment.World journal of gastroenterology · 2025Review
- Non-Invasive Prenatal Testing (NIPT): A Paradigm Shift in Prenatal Care.International journal of preventive 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with early detection playing a crucial role in improving survival rates. Artificial intelligence (AI), particularly in medical image analysis, has emerged as a potential tool for HCC diagnosis and surveillance. Recent advancements in deep learning-driven medical imaging have demonstrated significant potential in enhancing early HCC detection, particularly in ultrasound (US)-based surveillance.
methodThis review provides a comprehensive analysis of the current landscape, challenges, and future directions of AI in HCC surveillance, with a specific focus on the application in US imaging. Additionally, it explores AI's transformative potential in clinical practice and its implications for improving patient outcomes.
resultsWe examine various AI models developed for HCC diagnosis, highlighting their strengths and limitations, with a particular emphasis on deep learning approaches. Among these, convolutional neural networks have shown notable success in detecting and characterising different focal liver lesions on B-mode US often outperforming conventional radiological assessments. Despite these advancements, several challenges hinder AI integration into clinical practice, including data heterogeneity, a lack of standardisation, concerns regarding model interpretability, regulatory constraints, and barriers to real-world clinical adoption. Addressing these issues necessitates the development of large, diverse, and high-quality data sets to enhance the robustness and generalisability of AI models.
conclusionsEmerging trends in AI for HCC surveillance, such as multimodal integration, explainable AI, and real-time diagnostics, offer promising advancements. These innovations have the potential to significantly improve the accuracy, efficiency, and clinical applicability of AI-driven HCC surveillance, ultimately contributing to enhanced patient outcomes.
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.