ReviewHeliyon2022
Artificial intelligence applications in pathological diagnosis of gastric cancer.
Review in Heliyon, 2022. 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
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
8 citing papers in PubMed.
- Machine Learning-Based Survival Prediction Models for Young Patients With Gastric Cancer: Model Development and Validation Study.JMIR cancer · 2026Article
- Transforming Gastric Biopsy Diagnostics: Integrating Omics Technologies and Artificial Intelligence.Biomedicines · 2026Article
- Evaluating ChatGPT's recommendations for systematic treatment decisions in recurrent or metastatic head and neck squamous cell carcinoma: Perspectives from experts and junior doctors.International journal of cancer · 2025Article
- Global research trends on the association between gastric cancer and chronic atrophic gastritis: a bibliometric analysis.Discover oncology · 2025Article
- Innovative laboratory techniques shaping cancer diagnosis and treatment in developing countries.Discover oncology · 2025Review
- A large histological images dataset of gastric cancer with tumour microenvironment annotation for AI.Scientific data · 2025Article
- Navigating the Future: A Comprehensive Review of Artificial Intelligence Applications in Gastrointestinal Cancer.Cureus · 2024Review
- ChatGPT in glioma adjuvant therapy decision making: ready to assume the role of a doctor in the tumour board?BMJ health & care informatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Globally, gastric cancer is the third leading cause of death from tumors. Prevention and individualized treatment are considered to be the best options for reducing the mortality rate of gastric cancer. Artificial intelligence (AI) technology has been widely used in the field of gastric cancer, including diagnosis, prognosis, and image analysis. Eligible papers were identified from PubMed and IEEE up to April 13, 2022. Through the comparison of these articles, the application status of AI technology in the diagnosis of gastric cancer was summarized, including application types, application scenarios, advantages and limitations. This review presents the current state and role of AI in the diagnosis of gastric cancer based on four aspects: 1) accurate sampling from early diagnosis (endoscopy), 2) digital pathological diagnosis, 3) molecules and genes, and 4) clinical big data analysis and prognosis prediction. AI plays a very important role in facilitating the diagnosis of gastric cancer; however, it also has shortcomings such as interpretability. The purpose of this review is to provide assistance to researchers working in this domain.
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.