In remote and underserved communities where access to specialized medical care is limited, early detection of serious conditions like stomach cancer remains a major challenge. A new AI-powered system has shown promise in identifying individuals at high risk for stomach cancer using only upper endoscopic images—non-invasive visual data collected during routine screenings. These regions often lack access to advanced diagnostic tools, trained specialists, and timely follow-up care, making it difficult to detect cancer in its early, treatable stages. The AI system, developed by researchers and clinicians, analyzes endoscopic images to detect subtle signs of precancerous lesions and early-stage tumors that might be missed by less experienced practitioners. The technology uses deep learning algorithms trained on thousands of annotated endoscopic images from diverse populations, enabling it to recognize patterns associated with gastric abnormalities with high accuracy. In clinical trials, the AI demonstrated performance comparable to that of experienced gastroenterologists, even in low-resource settings where medical expertise is scarce. By providing real-time analysis during endoscopic procedures, the system can flag suspicious areas immediately, helping local healthcare providers make faster, more informed decisions. This reduces the need for patients to travel long distances for specialist consultations and enables earlier interventions that significantly improve survival rates. Importantly, the AI is designed to work with low-cost endoscopy equipment and minimal infrastructure, making it suitable for deployment in rural clinics and mobile health units. It also supports multiple languages and adapts to variations in patient anatomy and disease presentation across different populations. The system represents a major step forward in leveraging AI to address health disparities. By bringing expert-level diagnostic capabilities to areas with the fewest resources, it has the potential to save thousands of lives each year and transform how early cancer detection is delivered in the most vulnerable communities.