Artificial intelligence in gastrointestinal endoscopy: Focus on analytical depth and endoscopist training

We read the recent minireview by Ding et al . This review provided a structured introduction to the applications of artificial intelligence (AI) in gastrointestinal endoscopy while emphasizing the technical solutions for imaging hurdles. However, we identified some areas that were lacking analytical depth. Specifically, the review oversimplified machine learning and deep learning models (e.g. , generative adversarial networks misclassification) and failed to deeply analyze the explanations for missed tumor rates and the critical role of data quality/bias. In this article, we stress that the potential of AI extends beyond diagnostics and highlight its emerging and crucial role in endoscopist training, skill development, and proficiency enhancement. We conclude that future AI adoption depends on robust multicenter trials and the implementation of AI-assisted educational platforms.