Accurate histology prediction before resection of colorectal polyps: Is artificial intelligence shaping the future of...

Artificial intelligence (AI) is increasingly entering colonoscopy practice, with computer-aided detection (CADe) systems improving polyp and adenoma detection. However, the next challenge is not only to detect more lesions, but to determine in real time which lesions require resection, histopathological assessment, surveillance adjustment or surgical referral. Computer-aided diagnosis (CADx) systems aim to support this step by predicting colorectal polyp histology before resection and enabling optical diagnosis strategies such as "resect-and-discard" and "diagnose-and-leave". The clinical value of CADx should not be judged by diagnostic accuracy alone. To become clinically meaningful, CADx must safely guide management decisions, meet established ASGE and ESGE thresholds, integrate into real-time workflow and remain valid across endoscopy platforms, imaging modalities, lesion subtypes and operator expertise. Although systems such as CAD EYE, GI Genius, POLAR and endocytoscopy-based algorithms show promising diagnostic performance, recent meta-analyses suggest that CADx has not yet provided clear incremental benefit for "diagnose-and-leave" or "resect-and-discard" strategies when added to endoscopist assessment. This may reflect high baseline confidence among endoscopists, but also current limitations including binary classification schemes, inconsistent handling of sessile serrated lesions, lack of calibrated confidence scores and limited explainability. Beyond diminutive-polyp characterisation, AI is also being explored for invasion-depth prediction in larger or suspicious colorectal lesions, where incorrect predictions may lead to undertreatment or overtreatment. Moreover, AI-assisted colonoscopy remains dependent on high-quality mucosal exposure, adequate bowel preparation, careful inspection and trained endoscopists. Overall, CADx remains promising, but broader implementation requires prospective real-world validation, explainable and interoperable systems, robust human-AI interaction and clinically relevant outcomes before it can safely substitute histopathology in selected settings.