ReviewLiver research (Beijing, China)2026
Artificial intelligence in hepatocellular carcinoma diagnosis and AI-assisted treatment support: Current landscape and future directions.
Review in Liver research (Beijing, China), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma (HCC) remains challenging to diagnose and manage, particularly at an early stage when curative treatment is most feasible. Artificial intelligence (AI) offers tools for integrating histopathology, radiology, laboratory testing, demographics, and electronic health records, while also creating opportunities for clinician-supervised AI-assisted procedural support. Deep learning applied to digitized pathology slides can support tumor subtype classification, grading, and microvascular invasion assessment. AI-driven imaging methods may support liver and lesion segmentation, small-nodule detection in cirrhotic livers, and non-invasive risk stratification when combined with radiomics and clinical variables. Emerging foundation models, large language models (LLMs), and agentic approaches may help structure evidence, summarize longitudinal records, and support guideline-aligned decision workflows. In AI-assisted treatment-support settings, robotic surgery, endoscopic platforms, and image-guided interventions should be viewed as environments for AI-assisted planning, navigation, documentation, and quality control rather than autonomous treatment systems. Most evidence remains retrospective, single-center, and vulnerable to domain shift. Other limitations include LLM/agentic reliability failures, limited interpretability, privacy constraints, computational burden, incomplete workflow integration, workforce readiness gaps, and uncertain clinical benefit. Future progress will require diverse multimodal datasets, prospective multicenter validation, explainable and lightweight algorithms, standardized data alignment, rigorous governance, patient-centered workflow implementation, staff training, and careful separation of technical feasibility from clinically meaningful outcomes. Responsibly deployed AI may support diagnostic consistency, workflow efficiency, and human-AI collaboration in HCC care.
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.