1055/a-2760-4781 We are grateful to Yu et al. for their outstanding comments 1 , particularly their thoughtful emphasis on transparency, usability, scalability, safety, and workflow integration. The Matsu Islands have implemented a series of online artificial intelligence (AI) interpretation systems 1 2 3 . Physicians are informed about limitations of each model, especially regarding their role as decision support tools rather than standalone diagnostic systems. Displayed on the mobile picture archiving and communication system platform, the per-image interpretations with heatmaps, and the per-patient probability-based diagnoses, including the applied thresholds, are readily accessible to physicians through instructions that report the sensitivity and specificity of diagnosis. The models were developed using high performance computing resources. After optimization, the final models required approximately 10 GB of RAM, two central processing unit cores, and 10 GB of storage, enabling graphics processing unit-free computation. Given the relatively stable prevalence of Helicobacter pylori infection and premalignant gastric conditions in typical Asian populations, the predictive values observed in real-world practice are expected to closely align with those reported in the study. Regarding scalability, images were collected from a relatively monopolized endoscopy system with consistent imaging conditions. The model was trained using data from multiple hospitals 4 , resembling a federated learning-like approach and reducing impact of site-to-site variability. Nonetheless, real-world benefits and potential harms must continue to be monitored through ongoing safety and quality assurance procedures. These probability scores and categorical flags can be stored directly in the electronic health record, allowing endoscopists to view AI-derived information alongside routine reports. Existing approaches often characterize AI mainly by its technical functions, with an emphasis on fulfilling regulatory requirements. AI governance should function as an enabling architecture that aligns developers, clinicians, institutions, and regulators toward shared population health goals 5 . Further work is needed to establish these interconnections and feedback loops and to promote trust, patient safety, innovation, and equitable diffusion of AI technologies. Article published online: 20 February 2026 © 2026. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
Lee et al. (Fri,) studied this question.