Artificial intelligence in predicting colorectal polyp histology: Systematic review and meta-analysis of diagnostic accuracy in real-time procedures

Table 1 PICO (Population, Intervention, Comparison, Outcome) framework

Component	Definition	Criteria for inclusion	Criteria for exclusion
Research question	What is the diagnostic accuracy and clinical impact of AI-based real-time histology prediction for colorectal polyps compared to conventional histopathology and endoscopists?	Studies must specifically investigate AI assisted systems for real time polyp histology prediction	Studies that do not focus on real-time AI applications in live colonoscopy settings
Population	Patients undergoing colonoscopy with colorectal polyp detection and histology prediction	(1) Adults (≥ 18 years) undergoing colonoscopy; (2) Studies involving patients with colorectal polyps (adenomatous, hyperplastic, sessile serrated); and (3) Human subjects (no in vitro or animal studies)	(1) Studies focusing on animal models, in vitro, or simulation-based research; and (2) Pediatric studies (patients < 18 years)
Intervention	AI-based systems for real-time histology prediction of colorectal polyps	(1) AI assisted colonoscopy systems for polyp detection and classification; (2) Machine learning and deep learning models (e.g., convolutional neural networks); and (3) AI enhanced imaging techniques (e.g., narrow-band imaging, endocytoscopy)	AI models used only for retrospective analysis (not real-time)
Comparison	Standard histopathological methods or expert endoscopists’ assessments	(1) Histopathological examination as the gold standard; (2) Comparison with experienced endoscopists’ accuracy; and (3) Conventional endoscopy methods without AI assistance	AI models compared only with other AI models (without human or histological reference)
Outcome	Diagnostic accuracy of AI systems in polyp histology prediction	Primary outcomes: (1) Sensitivity, specificity, accuracy, and negative predictive value of AI models; and (2) Adenoma detection rate. Secondary outcomes: (1) Reduction in unnecessary polypectomies; (2) Interobserver variability between AI and human experts; and (3) Time efficiency and cost-effectiveness of AI-assisted endoscopy	(1) Studies with incomplete or insufficient clinical validation of AI performance; and (2) Studies that do not report key diagnostic accuracy metrics (e.g., missing sensitivity, specificity, or adenoma detection rate)

AI: Artificial intelligence.