PURPOSE OF REVIEW: The rising prevalence of obesity, now affecting over 40% of U.S. adults, poses critical implications for colorectal cancer screening, as obesity increases the risk of both colorectal adenomas and cancer. Despite these elevated risk...
Artificial intelligence (AI) holds immense potential to transform gastrointestinal endoscopy by reducing manual workload and enhancing procedural efficiency. However, the development of robust AI algorithms is hindered by limited access to high-quali...
BACKGROUND: Colorectal cancer (CRC) is the fourth most common cancer in the United Kingdom. The five-year survival rate from CRC is only 10% when discovered at a late stage, but can exceed 90% if diagnosed early. Symptoms related to CRC can be non-sp...
Colonoscopy is the leading endoscopic technique when it comes to implementing artificial intelligence-based tools to optimize the procedure. However, no database consisting of the colonoscope's coordinates exists, allowing for a mapping with timestam...
Journal of cancer research and clinical oncology
Jul 9, 2025
BACKGROUND: Colon cancer remains a leading cause of cancer-related mortality globally, highlighting the urgent need for advanced diagnostic methods to improve early detection and patient outcomes.
Currently, colonoscopy stands as the most efficient approach for detecting colorectal polyps. In clinical diagnosis, colorectal cancer is closely related to colorectal polyps. Therefore, precise segmentation of polyps holds paramount importance for t...
Accurate real-time optical diagnosis that distinguishes neoplastic from non-neoplastic colorectal lesions during colonoscopy can lower the costs of pathological assessments, prevent unnecessary polypectomies, and help avoid adverse events. Using a mu...
Colonoscopy is a crucial tool for evaluating lower gastrointestinal disease, monitoring high-risk patients for colorectal neoplasia, and screening for colorectal cancer. In the United States, over 14 million colonoscopies are performed annually, with...
INTRODUCTION: The implementation of computer-aided detection (CADe) systems has resulted in a growing number of young endoscopists being trained using AI-enhanced devices. The potential impact of AI-enhanced training on the trainees' future performan...
OBJECTIVE: To systematically evaluate large language models (LLMs) for automated information extraction from gastroscopy and colonoscopy reports through prompt engineering, addressing their ability to extract structured information, recognize complex...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.