In the era of digital medicine, artificial intelligence (AI) has gradually become a core driving force for the innovation and development of the medical industry. In the field of digestive and urinary endoscopy, the integration of AI technology has broken the bottleneck of traditional manual diagnosis, realized the transformation from "subjective judgment" to "intelligent precision", and opened a new chapter in the precision diagnosis of digestive and urinary diseases. The combination of AI and endoscopy not only improves the efficiency and accuracy of diagnosis but also solves the problems of uneven levels of clinicians and insufficient medical resources in remote areas, promoting the equalization of medical services.
Traditional digestive and urinary endoscopy diagnosis mainly relies on the subjective judgment of clinicians, which is greatly affected by factors such as the experience, energy and professional level of doctors. In gastroenterology, the early lesions of the digestive tract (such as early gastric cancer, intestinal polyps) often have no obvious characteristics, and it is easy to be missed by inexperienced doctors during endoscopy; in urology, the identification of small ureteral stones and early bladder tumors requires high professional ability of doctors, and the misdiagnosis rate is relatively high in primary medical institutions. In addition, the number of endoscopy operations in large hospitals is huge, and clinicians often face the problem of fatigue, which further increases the risk of missed diagnosis and misdiagnosis.
The emergence of AI-assisted endoscopic diagnosis technology has effectively solved the above problems. By training a large number of endoscopic image data (including normal tissues, benign lesions, malignant lesions, etc.), AI algorithms can quickly identify and mark abnormal tissues, and even distinguish subtle differences between benign and malignant lesions, which is difficult for manual diagnosis. At present, AI-assisted endoscopy systems have been widely used in the diagnosis of digestive tract polyps, early gastric cancer, bladder cancer, ureteral tumors and other diseases, showing excellent clinical performance.
Taking the AI-assisted gastrointestinal endoscopy system as an example, the system can realize real-time detection of gastrointestinal lesions during the endoscopy operation. When the endoscope captures images of the digestive tract, the AI algorithm can analyze the images in milliseconds, mark the suspected lesion area with a red frame, and prompt the clinician to focus on observation. According to clinical data, the system can improve the detection rate of early gastric cancer by 20%-30%, and the detection rate of intestinal polyps by more than 15%, especially for small polyps with a diameter of less than 5mm, which has a more obvious auxiliary effect. In urology, the AI-assisted ureteroscopic diagnosis system can accurately identify tiny ureteral stones and early ureteral tumors, and can distinguish between stones and tumor tissues, providing a reliable basis for clinicians to formulate treatment plans.
The core advantage of AI-assisted endoscopy lies in its "high efficiency, high accuracy and continuity". Unlike clinicians who will experience fatigue after long-term work, the AI system can maintain a stable diagnostic level 24 hours a day, which is particularly important for large-scale physical examinations and high-volume endoscopy operations. In addition, the AI system can record and analyze the endoscopic images in detail, form a diagnostic report automatically, and reduce the workload of clinicians, allowing them to focus more on the treatment of patients.
However, the popularization and application of AI-assisted endoscopy still face some challenges. On the one hand, the training of AI algorithms requires a large number of high-quality labeled endoscopic image data, but the current data resources are relatively scattered, and there is a lack of unified standards; on the other hand, the interpretability of AI diagnosis results is insufficient, and clinicians still need to make the final judgment based on their own experience, which limits the further promotion of AI technology. In addition, the cost of AI equipment is relatively high, which is difficult for some primary medical institutions to bear.
With the continuous improvement of AI technology and the gradual improvement of medical data standards, these problems will be gradually solved. In the future, AI-assisted endoscopy will move towards more intelligent and personalized directions. The combination of AI and big data will realize the prediction and early intervention of digestive and urinary diseases; the integration of AI and robot technology will realize intelligent operation of endoscopy, further improving the accuracy and safety of treatment. It is believed that with the deep integration of AI and endoscopy technology, the precision diagnosis and treatment level of digestive and urinary diseases will be comprehensively improved, bringing better medical services to patients.
