ReviewNature reviews. Gastroenterology & hepatology2024
Artificial intelligence in liver cancer - new tools for research and patient management.
Review in Nature reviews. Gastroenterology & hepatology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 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
40 citing papers in PubMed, 49 citations in OpenAlex.
- Prognostic significance of systemic immunoinflammatory biomarkers and pathological features in clear cell renal cell carcinoma: a multicenter study.Annals of medicine · 2026Trial
- Response evaluation to systemic therapy in HCC: Current challenges and future perspectives.Hepatology (Baltimore, Md.) · 2026Review
- Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.Cancer research · 2026Article
- Personalised decision-making in hepatocellular carcinoma: conceptual evolution over two decades.BJS open · 2026Review
- The versatile interplay between steatotic liver disease and liver cancer.Nature reviews. Cancer · 2026Review
- Article
- Transforming perioperative care: The current landscape and future trajectory of artificial intelligence in anesthesia-A narrative review.The Journal of international medical research · 2026Review
- Transformer-Driven Explainable Deep Learning with Quantitative Attribution Validation for Liver Tumor Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Machine learning outperforms serum creatinine for early risk stratification of hepatorenal syndrome: a prospective single-center validation.Hepatology international · 2026Article
- Multiphasic Evidential Decision-Making Matrix (MedMax) for Intrahepatic Cholangiocarcinoma: A Single-Center Validation Study.Cancers · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- Primary Liver Cancer Trends Worldwide and in China: Analysis of GLOBOCAN 2022 Data and Disease Management Implications.Portal hypertension & cirrhosis · 2026Review
- A deep learning radiopathomic signature predicts recurrence risk of hepatocellular carcinoma after hepatectomy.Communications biology · 2026Article
- Interpretable machine learning-driven optimization of physicochemical properties in hydrogel scaffolds to promote macrophage polarization.Biomaterials translational · 2026Article
- Advances in AI for detecting pulmonary inflammation and perioperative medicine: a mini-review.Frontiers in medicine · 2026Review
- Insights into artificial intelligence-based digital pathology in hepatobiliary and pancreatic cancer.Frontiers in oncology · 2026Review
- Leveraging AI and Natural Compounds: Innovative Approaches in the Diagnosis and Treatment of Hepatocellular Carcinoma.Current pharmaceutical design · 2026Review
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- Artificial intelligence predicts outcome-related molecular profiles and vascular invasion in hepatocellular carcinoma.JHEP reports : innovation in hepatology · 2025Article
- Material-based neuroimaging and biomarker detection for central nervous system disorder.Materials today. Bio · 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
5 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver cancer has high incidence and mortality globally. Artificial intelligence (AI) has advanced rapidly, influencing cancer care. AI systems are already approved for clinical use in some tumour types (for example, colorectal cancer screening). Crucially, research demonstrates that AI can analyse histopathology, radiology and natural language in liver cancer, and can replace manual tasks and access hidden information in routinely available clinical data. However, for liver cancer, few of these applications have translated into large-scale clinical trials or clinically approved products. Here, we advocate for the incorporation of AI in all stages of liver cancer management. We present a taxonomy of AI approaches in liver cancer, highlighting areas with academic and commercial potential, and outline a policy for AI-based liver cancer management, including interdisciplinary training of researchers, clinicians and patients. The potential of AI in liver cancer is immense, but effort is required to ensure that AI can fulfil expectations.
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.