The recognition mechanism of artificial intelligence (AI) is an interesting topic in understanding artificial intelligence neural networks and their application in treatment. Some layered neural networks can recognize cancer through deep learning algorithms. It will be interesting to think about whether human insights and artificial intelligence attention are related, which is one of the main points in this article.

To achieve clinical implementation of AI, several steps need to be considered. Colling et al. presented a tentative roadmap for the routine use of AI in pathology practice. They highlighted key aspects of designing and implementing AI into daily practice. Steps related to design innovation, ethics, financing, development, validation, regulation, implementation, and workforce impact were closely examined. For pathology image analysis, various issues exist in the implementation of these steps, which will prevent AI from being implemented in clinical practice for gastrointestinal oncology.

At present, functional magnified endoscopic staining has become the clinical method of first choice for the early diagnosis of esophageal squamous cell carcinoma. However, the accuracy of functional magnified staining endoscopic function in the early diagnosis of esophageal squamous cell carcinoma is limited by the experience and qualifications of the endoscopist. Artificial intelligence can help inexperienced endoscopists intelligently analyze medical images, detect and/or classify lesions, and improve diagnostic accuracy using advanced algorithms. variation math.

Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of artificial intelligence is expected to cause a revolution in various fields of medicine, including gastrointestinal pathology such as stomach cancer and colorectal cancer.