ReviewJournal of the National Cancer Center2025
The application of artificial intelligence in upper gastrointestinal cancers.
Review in Journal of the National Cancer Center, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed.
- Single-Cell Transcriptomic Analysis Identifies an OLFM4-Associated Gastric Cancer Cell State with Palmitoylation-Related Signatures and Altered Metabolic Activities.Biomolecules · 2026Article
- Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Recent advances in drug repurposing for cancer immunomodulation emerging strategies, mechanistic insights, and clinical translation.Frontiers in oncology · 2026Review
- Integrating single-cell transcriptomics to construct an oncogene-driven prognostic model and elucidate metabolic-immune crosstalk in hepatocellular carcinoma.Molecular & cellular oncology · 2026Article
- Macroscopic growth patterns and their molecular implications in gastric carcinoma: toward risk stratification via borrmann classification.BMC gastroenterology · 2025Article
- Artificial intelligence in medical imaging empowers precision neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma.Journal for immunotherapy of cancer · 2025Review
- Regional and patient-level determinants of endoscopic utilization in rural healthcare: a multi-level analysis.Frontiers in oncology · 2025Article
- Artificial intelligence in advanced gastric cancer: a comprehensive review of applications in precision oncology.Frontiers in oncology · 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
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Upper gastrointestinal cancers, mainly comprising esophageal and gastric cancers, are among the most prevalent cancers worldwide. There are many new cases of upper gastrointestinal cancers annually, and the survival rate tends to be low. Therefore, timely screening, precise diagnosis, appropriate treatment strategies, and effective prognosis are crucial for patients with upper gastrointestinal cancers. In recent years, an increasing number of studies suggest that artificial intelligence (AI) technology can effectively address clinical tasks related to upper gastrointestinal cancers. These studies mainly focus on four aspects: screening, diagnosis, treatment, and prognosis. In this review, we focus on the application of AI technology in clinical tasks related to upper gastrointestinal cancers. Firstly, the basic application pipelines of radiomics and deep learning in medical image analysis were introduced. Furthermore, we separately reviewed the application of AI technology in the aforementioned aspects for both esophageal and gastric cancers. Finally, the current limitations and challenges faced in the field of upper gastrointestinal cancers were summarized, and explorations were conducted on the selection of AI algorithms in various scenarios, the popularization of early screening, the clinical applications of AI, and large multimodal models.
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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.