Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects
Clinical Snapshot
PICO Framework
| P — Population | Patients undergoing endoscopic examination for gastric cancer screening or diagnosis |
| I — Intervention | Artificial intelligence-assisted diagnostic systems for endoscopic imaging |
| C — Comparator | Conventional endoscopic diagnosis by clinicians |
| O — Outcomes | Detection rate of early gastric cancer, diagnostic accuracy, sensitivity, specificity, and patient survival outcomes |
Bottom Line
This narrative review examines the potential of artificial intelligence systems to improve early gastric cancer detection during endoscopy. While the authors suggest that AI-assisted diagnosis may enhance detection rates and patient outcomes, the review lacks systematic methodology and provides no quantitative evidence synthesis. The absence of search strategy, quality assessment, or specific effect sizes limits the clinical utility of these findings. The authors acknowledge significant challenges remain before widespread clinical implementation. For Australian clinicians, any AI diagnostic tools would require TGA approval and careful validation in local populations. Current evidence is insufficient to recommend routine clinical adoption, though the technology shows promise for future development. Clinicians should continue relying on established endoscopic techniques while monitoring emerging evidence from well-designed systematic reviews and randomised controlled trials.
Key Findings
P Value: Not reported
Effect Size: Not quantified
Primary Outcome: Improved detection rate of early gastric cancer with AI assistance
Nnt Or Sensitivity: Not specified - general statement about improved survival rates
Confidence Interval: Not provided
Clinical Application
Limited by current technology availability, training requirements, and regulatory approval processes Would require TGA approval for diagnostic devices; relevant given Australia's gastric cancer burden in certain populations, particularly those of Asian descent Patients undergoing upper endoscopy for gastric cancer screening, particularly in high-risk populations
Abstract
Gastric cancer (GC) occupies the first few places in the world among tumors in terms of incidence and mortality, causing serious harm to human health, and at the same time, its treatment greatly consumes the health care resources of all countries in the world. The diagnosis of GC is usually based on histopathologic examination, and it is very important to be able to detect and identify cancerous lesions at an early stage, but some endoscopists' lack of diagnostic experience and fatigue at work lead to a certain rate of under diagnosis. The rapid and striking development of Artificial intelligence (AI) has helped to enhance the ability to extract abnormal information from endoscopic images to some extent, and more and more researchers are applying AI technology to the diagnosis of GC. This initiative has not only improved the detection rate of early gastric cancer (EGC), but also significantly improved the survival rate of patients after treatment. This article reviews the results of various AI-assisted diagnoses of EGC in recent years, including the identification of EGC, the determination of differentiation type and invasion depth, and the identification of borders. Although AI has a better application prospect in the early diagnosis of ECG, there are still major challenges, and the prospects and limitations of AI application need to be further discussed.
References
- 1.Lei, C., Sun, W., Wang, K., Weng, R., Kan, X., & Li, R. (2025). Artificial intelligence-assisted diagnosis of early gastric cancer: present practice and future prospects. Annals of Medicine. https://doi.org/10.1053/j.gastro.2018.04.003
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