Augmenting colonoscopy with artificial intelligence and robotic assistance: a human-factors scoping review toward a task-based shared autonomy paradigm.

Colonoscopy is central to colorectal cancer prevention but remains a high-demand psychomotor and cognitive procedure, characterized by operator dependency, variable performance, and substantial physical and mental workload. Increasing evidence links procedural quality and safety to clinician-related factors, including musculoskeletal strain, fatigue, and non-technical skills. Concurrently, artificial intelligence (AI) and robotic technologies are emerging as potential tools to address these limitations. We conducted a scoping review with narrative synthesis of literature on human factors, ergonomics, AI, robotic technologies, and shared autonomy in colonoscopy and surgical robotics. Evidence synthesis: Colonoscopy performance remains limited by non-negligible miss rates and variability driven by both technical and human factors. Ergonomic constraints and cognitive workload contribute significantly to operator strain and may affect vigilance and decision-making. AI systems-including computer-aided detection (CADe), diagnosis (CADx), and quality monitoring-have demonstrated improvements in detection metrics and procedural standardisation, although concerns regarding cognitive offloading and potential deskilling persist. In parallel, emerging robotic and soft-robotic platforms aim to enhance navigation, improve procedural ergonomics, and reduce operator musculoskeletal strain, although clinical evidence remains limited. These technologies converge within a task-based shared-autonomy paradigm integrating physical, perceptual, and cognitive augmentation, with different procedural tasks operating at different levels of human-system control. A task-based shared-autonomy framework may provide a coherent model for integrating AI and robotics into colonoscopy while preserving human supervision and clinical judgement. Future developments should prioritise human-centred design, workflow integration, and multidimensional evaluation strategies to ensure safe and effective clinical implementation.