ReviewWorld journal of gastroenterology2020
Application of artificial intelligence in the diagnosis and treatment of hepatocellular carcinoma: A review.
Review in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 3 of them syntheses that pooled it.
What it found
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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.
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Who cites it
27 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The use of machine learning in transarterial chemoembolisation/transarterial embolisation for patients with intermediate-stage hepatocellular carcinoma: a systematic review.La Radiologia medica · 2025Pooled it
- Mapping the knowledge domains of literature on hepatocellular carcinoma and liver failure: a bibliometric approach.Frontiers in oncology · 2025Pooled it
- Artificial intelligence in liver cancer research: a scientometrics analysis of trends and topics.Frontiers in oncology · 2024Pooled it
- Selection of proper artificial intelligence techniques developed for CT scan image analysis of liver cancer using fuzzy AHP-TOPSIS.BMC medical imaging · 2026Article
- Progress in fully automated abdominal CT interpretation-an update over the past decade.Abdominal radiology (New York) · 2026Review
- Machine Learning-Based Algorithm for Tacrolimus Dose Optimization in Hospitalized Kidney Transplant Patients.Diagnostics (Basel, Switzerland) · 2025Article
- Multiphase Computed Tomography Scan Findings for Artificial Intelligence Training in the Differentiation of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma Based on Interobserver Agreement of Expert Abdominal Radiologists.Diagnostics (Basel, Switzerland) · 2025Article
- Review
- Multidisciplinary Team Management of Hepatocellular Carcinoma in the MENA Region: Current Practices, Challenges, and Gaps.Journal of hepatocellular carcinoma · 2025Article
- Combining a deep learning model with clinical data better predicts hepatocellular carcinoma behavior following surgery.Journal of pathology informatics · 2024Article
- A review of deep learning approaches for multimodal image segmentation of liver cancer.Journal of applied clinical medical physics · 2024Review
- Hepatocellular Carcinoma Surveillance Strategies: Major Guidelines and Screening Advances.Cancers · 2024Review
- Review
- Multidisciplinary approach for hepatocellular carcinoma patients: current evidence and future perspectives.Journal of liver cancer · 2024Article
- Artificial Intelligence in Liver Diseases: Recent Advances.Advances in therapy · 2024Review
- Response Assessment of Primary Liver Tumors to Novel Therapies: an Imaging Perspective.Journal of gastrointestinal surgery : official journal of the Society for Surgery of the Alimentary Tract · 2023Review
- Current status and future perspectives of radiomics in hepatocellular carcinoma.World journal of gastroenterology · 2023Review
- Revisiting artificial intelligence diagnosis of hepatocellular carcinoma with DIKWH framework.Frontiers in genetics · 2023Review
- Early Differentiation of Irreversible Electroporation Ablation Regions With Radiomics Features of Conventional MRI.Academic radiology · 2022Article
- Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery.Medicina (Kaunas, Lithuania) · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Although artificial intelligence (AI) was initially developed many years ago, it has experienced spectacular advances over the last 10 years for application in the field of medicine, and is now used for diagnostic, therapeutic and prognostic purposes in almost all fields. Its application in the area of hepatology is especially relevant for the study of hepatocellular carcinoma (HCC), as this is a very common tumor, with particular radiological characteristics that allow its diagnosis without the need for a histological study. However, the interpretation and analysis of the resulting images is not always easy, in addition to which the images vary during the course of the disease, and prognosis and treatment response can be conditioned by multiple factors. The vast amount of data available lend themselves to study and analysis by AI in its various branches, such as deep-learning (DL) and machine learning (ML), which play a fundamental role in decision-making as well as overcoming the constraints involved in human evaluation. ML is a form of AI based on automated learning from a set of previously provided data and training in algorithms to organize and recognize patterns. DL is a more extensive form of learning that attempts to simulate the working of the human brain, using a lot more data and more complex algorithms. This review specifies the type of AI used by the various authors. However, well-designed prospective studies are needed in order to avoid as far as possible any bias that may later affect the interpretability of the images and thereby limit the acceptance and application of these models in clinical practice. In addition, professionals now need to understand the true usefulness of these techniques, as well as their associated strengths and limitations.
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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.