From availability to adoption: Why endoscopist-level use determines the real-world impact of computer-aided detection in colonoscopy

Table 1 Integrated determinants of real-world effectiveness of computer-aided detection in colonoscopy: An evidence-informed conceptual synthesis

Domain	Key determinant	Integrated evidence synthesis	Impact on CADe effectiveness
Technology	CADe availability	The mere presence of an approved CADe system enables real-time polyp alerts but does not ensure improved detection outcomes without active user engagement	Necessary prerequisite but insufficient alone
	Alert characteristics	Systems with excessive false-positive alerts increase cognitive load and distract visual attention, whereas low false-positive systems facilitate trust and sustained use	Modulates trust and attention
Endoscopist behavior	Usage frequency	Consistent or high-frequency CADe use is associated with incremental improvements in adenoma detection, while intermittent or selective use yields attenuated benefit	Establishes a dose-response relationship
	Adoption intensity	Adoption intensity, rather than binary use, determines the magnitude of adenoma yield and detection consistency	Primary determinant of real-world impact
Endoscopist expertise	Baseline detection skill	CADe disproportionately benefits trainees and low baseline detectors, narrowing performance gaps with experts; benefits among high detectors are smaller and context-dependent	Differential effect by skill level
	Ceiling effect	In high-performing endoscopists, CADe gains may plateau or be limited to subtle lesions, reflecting a ceiling effect rather than lack of efficacy	Explains heterogeneous results
Human–AI interaction	Trust calibration	Balanced trust enhances responsiveness to meaningful alerts, whereas overreliance or skepticism reduces CADe utility	Determines effective alert utilization
	Cognitive load	Excessive alerts or workflow disruption increase mental burden and may impair visual search strategies	Can blunt or reverse benefit
Workflow integration	Procedural compatibility	Seamless integration into routine withdrawal and inspection patterns facilitates adoption, while disruptive interfaces reduce sustained use	Influences adherence
	Response behavior	Effective CADe use depends on how endoscopists respond to alerts, not merely alert presence	Mediates clinical translation
Procedural quality	Withdrawal time	Adequate withdrawal time synergizes with CADe to enhance detection, while rushed examinations limit AI benefit	Enables CADe amplification
	Bowel preparation	High-quality bowel preparation maximizes CADe sensitivity and downstream detection outcomes	Foundational modifier
Clinical outcomes	ADR	ADR improvement is most pronounced with consistent CADe adoption and among low-to-intermediate baseline detectors	Primary quality endpoint
	Adenomas per colonoscopy	High adoption intensity is associated with additional gains in adenoma yield beyond ADR alone	Reflects cumulative benefit
	Miss rate variability	CADe adoption reduces inter-endoscopist variability and fatigue-related declines when consistently applied	Improves quality stability
Implementation strategy	Passive deployment	Unstructured implementation without training or monitoring results in inconsistent use and muted benefit	Limits real-world effectiveness
Active adoption framework	Training, adherence monitoring, and workflow optimization promote sustained CADe use and maximize clinical value	Optimizes translation to practice

The determinants summarized in this table represent an integrated conceptual synthesis derived from heterogeneous evidence sources, including randomized controlled trials, real-world observational studies, and other literature, rather than a claim-by-claim systematic evidence map. ADR: Adenoma detection rate; AI: Artificial intelligence; CADe: Computer-aided detection.