Role of artificial intelligence in gastric diseases

Abstract

The integration of artificial intelligence (AI) in gastroenterology has evolved from basic computer-aided detection to sophisticated multimodal frameworks that enable real-time clinical decision support. This study presents AI applications in gastric disease diagnosis and management, highlighting the transition from domain-specific deep learning to general-purpose large language models. Our research reveals a key finding: AI effectiveness demonstrates an inverse relationship with user expertise, with moderate-expertise practitioners benefiting the most, whereas experts and novices show limited performance gains. We developed a clinical decision support system achieving 96% lesion detection internally and 82%-87% classification accuracy in external validation. Multimodal integration, which combines endoscopic images, clinical histories, laboratory results, and genomic data, enables comprehensive disease assessment and personalized treatment. The emergence of large language models with expanding context windows and multiagent architectures represents a paradigm shift in medical AI. Furthermore, emerging technologies are expanding AI’s potential applications, and feasibility studies on smart glasses in endoscopy training suggest opportunities for hands-free assistance, although clinical implementation challenges persist. This minireview addresses persistent limitations including geographic bias in training data, regulatory hurdles, ethical considerations regarding patient privacy and AI accountability, and the concentration of AI development among technology giants. Successful integration requires balancing innovation with patient safety, while preserving the irreplaceable role of human clinical judgment.

Keywords: Artificial intelligence; Endoscopy; Deep learning; Machine learning; Large language models; Gastric cancer

Core Tip: This minireview demonstrates that artificial intelligence (AI) in gastric disease diagnosis has reached clinical maturity, with systems achieving expert-level performance in cancer detection, precancerous lesion identification, and clinical outcome prediction. The key insight is that AI effectiveness is inversely correlated with user expertise, providing the greatest benefit to practitioners with moderate expertise. The emergence of general-purpose large language models (LLMs) represents a paradigm shift from developing custom AI models that require years of specialized training to leveraging pre-trained systems that clinicians can adapt within weeks without coding expertise. This democratization of AI technology through LLMs enables all medical professionals, regardless of their technical background, to access sophisticated AI capabilities, fundamentally changing how we integrate AI into practice.