ReviewJournal of gastric cancer2023
The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review.
Review in Journal of gastric cancer, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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.
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Who cites it
9 citing papers in PubMed.
- Could artificial intelligence-powered colonoscopies change the future of colorectal cancer screening?World journal of gastroenterology · 2025Review
- Predicting Anastomosis or Stump Leakage After Laparoscopic Gastrectomy: A Deep Learning Approach to Intraoperative Image Analysis.Journal of gastric cancer · 2025Article
- Revolutionizing upper gastrointestinal disease diagnosis: The transformative role of artificial intelligence in endoscopy.World journal of gastrointestinal endoscopy · 2025Review
- Artificial intelligence in gastrointestinal cancers: Diagnostic, prognostic, and surgical strategies.Cancer letters · 2025Review
- A Study of the Effectiveness of Apoptosis Biomarkers in the Diagnosis of Gastric Cancer in Kazakhstan: A Review of Systematic Evidence.Medical journal of the Islamic Republic of Iran · 2025Review
- Article
- Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy.Diagnostics (Basel, Switzerland) · 2024Article
- Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy.Diagnostics (Basel, Switzerland) · 2024Article
- An artificial intelligence system for comprehensive pathologic outcome prediction in early gastric cancer through endoscopic image analysis (with video).Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.
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