Gastric cancer remains one of the most prevalent and lethal malignancies worldwide,characterized by an insidious onset,challenges in early detection,and a poor prognosis in advanced stages.Conventional diagnostic approaches are often constrained by subjective interpretation and inherent limitations in accuracy and efficiency,rendering them insufficient to meet the demands of precision medicine.In recent years,the rapid advancement of artificial intelligence(AI),particularly deep learning(DL)-based techniques,has opened new avenues for the precise diagnosis and management of gastric cancer.Emerging evidence suggests that AI-assisted endoscopic systems significantly enhance lesion detection rates and diagnostic efficiency,while AI-driven radiomics models offer precise predictions of tumor invasion depth,lymph node involvement,and peritoneal metastasis.Additionally,AI-powered pathology analysis has markedly improved both diagnostic accuracy and efficiency.Moreover,integrative AI models leveraging multi-omics data have demonstrated great potential in predicting responses to chemotherapy and targeted therapies,as well as facilitating personalized prognostic assessments.However,despite these promising advancements,the clinical implementation of AI in gastric cancer remains hindered by challenges such as the lack of standardized datasets,limited model generalizability,and insufficient algorithm interpretability.This review systematically synthesized the latest advancements in AI applications for gastric cancer diagnosis,treatment response evaluation,and prognostic prediction.Furthermore,it critically examined key technical challenges in current AI methodologies and explored future directions in AI-driven precision medicine for gastric cancer.By addressing these challenges,we aimed to foster the widespread adoption and clinical translation of AI technologies,ultimately advancing precision oncology and improving patient outcomes.

Gastric cancer remains one of the most prevalent and lethal malignancies worldwide,characterized by an insidious onset,challenges in early detection,and a poor prognosis in advanced stages.Conventional diagnostic approaches are often constrained by subjective interpretation and inherent limitations in accuracy and efficiency,rendering them insufficient to meet the demands of precision medicine.In recent years,the rapid advancement of artificial intelligence(AI),particularly deep learning(DL)-based techniques,has opened new avenues for the precise diagnosis and management of gastric cancer.Emerging evidence suggests that AI-assisted endoscopic systems significantly enhance lesion detection rates and diagnostic efficiency,while AI-driven radiomics models offer precise predictions of tumor invasion depth,lymph node involvement,and peritoneal metastasis.Additionally,AI-powered pathology analysis has markedly improved both diagnostic accuracy and efficiency.Moreover,integrative AI models leveraging multi-omics data have demonstrated great potential in predicting responses to chemotherapy and targeted therapies,as well as facilitating personalized prognostic assessments.However,despite these promising advancements,the clinical implementation of AI in gastric cancer remains hindered by challenges such as the lack of standardized datasets,limited model generalizability,and insufficient algorithm interpretability.This review systematically synthesized the latest advancements in AI applications for gastric cancer diagnosis,treatment response evaluation,and prognostic prediction.Furthermore,it critically examined key technical challenges in current AI methodologies and explored future directions in AI-driven precision medicine for gastric cancer.By addressing these challenges,we aimed to foster the widespread adoption and clinical translation of AI technologies,ultimately advancing precision oncology and improving patient outcomes.

Based on the theory of engineering medicine,a consensus which takes the basic medical ethics of harm reduction as the starting point is proposed to addresses the current clinical problems of a wide variety of radiotherapy setup equipments and methods,large differences by principles,and inaccurate setup.The consensus is formed in two aspects.(1)Advocate coordination of multiple setup methods for joint setup;collect,compare,analyze and screen data on setup methods;determine the operational guidelines and methods for joint setup based on the principle of standardized and unified clinical consistency,with a view to achieving the clinical purpose of greatly ensuring the precision of radiotherapy setup and radiotherapy safety without relying on the golden standard.(2)Standardize the operational methods for tracing setup deviations,so that when the difference in setup leads to poor clinical consistency,the cause of deviation can be traced and the effectiveness of different setups can be screened.Based on the concept of engineering medicine,the consensus is expected to standardize the method of radiotherapy setup,realize accurate radiotherapy,improve treatment effect and show medical ethical care.

Based on the theory of engineering medicine,a consensus which takes the basic medical ethics of harm reduction as the starting point is proposed to addresses the current clinical problems of a wide variety of radiotherapy setup equipments and methods,large differences by principles,and inaccurate setup.The consensus is formed in two aspects.(1)Advocate coordination of multiple setup methods for joint setup;collect,compare,analyze and screen data on setup methods;determine the operational guidelines and methods for joint setup based on the principle of standardized and unified clinical consistency,with a view to achieving the clinical purpose of greatly ensuring the precision of radiotherapy setup and radiotherapy safety without relying on the golden standard.(2)Standardize the operational methods for tracing setup deviations,so that when the difference in setup leads to poor clinical consistency,the cause of deviation can be traced and the effectiveness of different setups can be screened.Based on the concept of engineering medicine,the consensus is expected to standardize the method of radiotherapy setup,realize accurate radiotherapy,improve treatment effect and show medical ethical care.