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Review
. 2025 Mar 21;25(1):111.
doi: 10.1186/s12935-025-03756-4.

The artificial intelligence revolution in gastric cancer management: clinical applications

Affiliations
Review

The artificial intelligence revolution in gastric cancer management: clinical applications

Runze Li et al. Cancer Cell Int. .

Abstract

Nowadays, gastric cancer has become a significant issue in the global cancer burden, and its impact cannot be ignored. The rapid development of artificial intelligence technology is attempting to address this situation, aiming to change the clinical management landscape of gastric cancer fundamentally. In this transformative change, machine learning and deep learning, as two core technologies, play a pivotal role, bringing unprecedented innovations and breakthroughs in the diagnosis, treatment, and prognosis evaluation of gastric cancer. This article comprehensively reviews the latest research status and application of artificial intelligence algorithms in gastric cancer, covering multiple dimensions such as image recognition, pathological analysis, personalized treatment, and prognosis risk assessment. These applications not only significantly improve the sensitivity of gastric cancer risk monitoring, the accuracy of diagnosis, and the precision of survival prognosis but also provide robust data support and a scientific basis for clinical decision-making. The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. Although most of the current AI-based models have not been widely used in clinical practice, with the continuous deepening and expansion of precision medicine, we have reason to believe that a new era of AI-driven gastric cancer care is approaching.

Keywords: Artificial intelligence; Diagnosis; Forecast; Gastric cancer; Research progress; Treat.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The relationship between artificial intelligence and machine learning and deep learning
Fig. 2
Fig. 2
Artificial intelligence is integrated into endoscopy workflow
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