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Colonoscopy is a key screening method for colorectal cancer (CRC), but its effectiveness is limited. Computer-aided detection (CADe) and computer-aided diagnostics (CADx), as part of an artificial intelligence (AI) system, improve the detection and optical characterization of lesions. This review maps and analyzes the evidence on the application of AI in colonoscopy, with a focus on the detection, segmentation, and characterization of colon neoplasms, available platforms, architectural models and implementation. The review was conducted in accordance with JBI and PRISMA-ScR guidelines, using the PCC framework. Meta-analyses, randomized controlled trials, systematic and narrative reviews, observational studies, guidelines, and consensus documents on the use of AI systems in different phases of colonoscopy were searched. CADe significantly improves adenoma detection and reduces the number of missed lesions. CADx, segmentation, depth of invasion assessment, and detection of learned lesions remain limited and heterogeneous. CADe has strong evidence for improving ADR, whereas current evidence for CADx remains insufficient to support a "resect-and-discard" strategy. Further cost-effectiveness studies are needed. Commercial platforms vary in their features and level of clinical validation. Colonoscopy using AI is a current topic with rapid development. This scoping review comprises heterogeneous literature covering clinical applications, technical aspects, the potential benefits and limitations of AI in improving colonoscopy performance and reducing the burden of colorectal cancer. Further trials involving diverse patient populations across different countries are needed to validate and extend the current evidence.