Artificial Intelligence-Driven Approaches to Endoscopic Gastric Cancer Detection: Current Progress and Future Directions.

Gastric cancer (GC) is one of the most prevalent and lethal cancers globally, with early detection being crucial for improving patient survival rates. Endoscopy remains the primary diagnostic tool for gastric cancer, but its accuracy is highly dependent on the endoscopist's skill. Misdiagnosis rates, caused by factors such as incomplete lesion recognition and operator errors, remain a significant challenge. Artificial intelligence (AI), particularly deep learning models like convolutional neural networks (CNNs), has shown substantial potential in enhancing the detection of gastric cancer during endoscopic procedures. AI-driven systems have demonstrated the ability to improve diagnostic accuracy, reduce missed lesions, and standardize assessments, regardless of endoscopist experience. Studies have highlighted AI's effectiveness, with some models achieving diagnostic accuracy comparable to senior practitioners. However, challenges such as data quality, false positives/negatives, geographical biases, and regulatory barriers need to be addressed for broader clinical implementation. Despite these limitations, ongoing advancements in AI technology, coupled with multinational research and improved dataset diversity, hold promise for improving the early detection of gastric cancer, enhancing patient outcomes, and optimizing clinical workflows.