ReviewJournal of clinical medicine2022
Artificial Intelligence in the Diagnosis of Hepatocellular Carcinoma: A Systematic Review.
Review in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 19 citations in OpenAlex.
- Selection of proper artificial intelligence techniques developed for CT scan image analysis of liver cancer using fuzzy AHP-TOPSIS.BMC medical imaging · 2026Article
- Hepatocellular Carcinoma in Southeast Asian Americans: Epidemiologic Trends, Screening Challenges, and Policy Implications.Healthcare (Basel, Switzerland) · 2026Review
- Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation.World journal of gastrointestinal oncology · 2026Article
- An interpretable machine learning model for diagnostic classification of liver cancer using multivariable clinical data.American journal of cancer research · 2026Article
- Artificial intelligence in clinical medicine: a state-of-the-art overview of systematic reviews with methodological recommendations for improved reporting.Frontiers in digital health · 2025Review
- A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.Scientific reports · 2024Article
- Epidemiology, pathophysiology and clinical aspects of Hepatocellular Carcinoma in MAFLD patients.Hepatology international · 2024Review
- Predicting microvascular invasion in hepatocellular carcinoma with a CT- and MRI-based multimodal deep learning model.Abdominal radiology (New York) · 2024Article
- Existing and emerging biomarkers in hepatocellular carcinoma: relevance in staging, determination of minimal residual disease, and monitoring treatment response: a narrative review.Hepatobiliary surgery and nutrition · 2024Review
- Deep Learning Methods in Medical Image-Based Hepatocellular Carcinoma Diagnosis: A Systematic Review and Meta-Analysis.Cancers · 2023Review
- Biomarkers for immunotherapy of hepatocellular carcinoma.Nature reviews. Clinical oncology · 2023Review
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 9 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Hepatocellular carcinoma ranks fifth amongst the most common malignancies and is the third most common cause of cancer-related death globally. Artificial Intelligence is a rapidly growing field of interest. Following the PRISMA reporting guidelines, we conducted a systematic review to retrieve articles reporting the application of AI in HCC detection and characterization. A total of 27 articles were included and analyzed with our composite score for the evaluation of the quality of the publications. The contingency table reported a statistically significant constant improvement over the years of the total quality score (
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.