[Challenges and strategies for the integration of artificial intelligence and endoscopy: focusing on colonoscopy practice].

Artificial intelligence (AI) has witnessed rapid development in the field of digestive endoscopy, especially for colonoscopy, where its application is the most mature. Based on deep learning algorithms, three major technical systems have been established in clinical practice, including computer-aided detection (CADe), computer-aided diagnosis (CADx), and computer-aided quality assessment (CAQ). Numerous randomized controlled trials have verified its prominent advantages in improving adenoma detection rate, reducing missed diagnosis risks, and assisting pathological prediction. Nevertheless, such benefits have not been fully reproduced in real-world studies, highlighting practical challenges in translating AI efficacy "validated in laboratories" into "usefulness in clinical settings". Based on clinical practice of colonoscopy, this article systematically reviews the current applications of AI. From the perspectives of inherent technical limitations of AI models and subjective factors in human-machine collaboration, it analyzes the underlying causes limiting the clinical benefits of AI in real-world scenarios. Furthermore, this study proposes a dual strategy of "exploring scenario-driven differentiated deployment" and "optimizing human-machine collaboration". On one hand, the appropriate application scope of AI should be rationally defined, and differentiated deployment should be carried out according to clinicians' experience levels and clinical scenarios. On the other hand, systematic training is required to help clinicians form scientific understanding and reasonable expectations of AI and establish an efficient human-machine collaboration model, so as to facilitate in-depth integration of AI and endoscopic techniques and promote its wide clinical implementation.