Artificial Intelligence-Driven Personalized Dietary Recommendations for Gastric Cancer High-Risk Populations: A Narrative Review

Why It Matters

AI‑powered nutrition could shift gastric cancer prevention from generic guidelines to individualized, data‑driven strategies, potentially lowering incidence and mortality.

Key Takeaways

• AI enhances diet guidance for diabetes, obesity; may aid cancer
• High‑salt, low‑fiber diets raise gastric cancer risk via microbiome
• ML models predict personal glycemic response, improving nutrition plans
• Data privacy, model opacity, and missing trials limit AI use
• Multi‑omics AI integration promises precise risk stratification

Pulse Analysis

Gastric cancer remains a leading cause of cancer death worldwide, with diet and the gastric microbiome playing pivotal roles in disease onset. Traditional prevention relies on population‑wide recommendations, yet high‑risk groups—defined by H. pylori infection, genetic mutations, or precancerous lesions—require more precise interventions. AI’s ability to synthesize heterogeneous data, from dietary logs to genomic profiles, offers a pathway to tailor nutrition advice that directly targets the biological pathways driving carcinogenesis, such as inflammation and nitrosamine formation.

In chronic disease arenas, AI‑driven platforms have already demonstrated measurable benefits: machine‑learning algorithms predict post‑prandial glucose spikes, enabling personalized carbohydrate selection that reduces diabetes complications, while deep‑learning models optimize diet‑microbiome interactions to support weight loss. Translating these successes to gastric cancer prevention involves integrating multi‑omics inputs—metabolomics, microbiome sequencing, and serologic biomarkers—into predictive models that flag individuals likely to progress along the Correa cascade. Such systems can generate dynamic meal plans, adjust for sodium intake, and recommend protective foods like fiber‑rich vegetables, thereby mitigating known dietary risk factors.

Despite the promise, widespread adoption faces hurdles. Model transparency is essential for clinicians to trust AI recommendations, and stringent data‑privacy safeguards must protect sensitive genetic and microbiome information. Moreover, the current evidence base lacks randomized trials that directly link AI‑guided nutrition to reduced gastric cancer incidence. Addressing these gaps will require collaborative research networks, standardized data pipelines, and equitable access to technology across diverse populations. If these challenges are met, AI could become a cornerstone of precision prevention, shifting the paradigm from reactive treatment to proactive, individualized dietary stewardship.