For individuals carrying Lynch syndrome mutations, colonoscopy surveillance is a cornerstone of cancer prevention — yet the biology of this hereditary condition makes it uniquely challenging. Unlike sporadic colorectal cancer, Lynch syndrome is characterized by an accelerated adenoma-to-carcinoma pathway and a disproportionate prevalence of flat, subtle precursor lesions that evade even experienced endoscopists. The question of whether AI-assisted detection tools can close that gap is now drawing serious scrutiny.
This narrative review in the International Journal of Cancer synthesizes current evidence on AI-powered computer-aided detection (CADe) in Lynch syndrome surveillance, drawing primarily on two randomized controlled trials — CADLY and TIMELY. Across average-risk colorectal screening populations, CADe has reliably improved adenoma detection rates, reducing miss rates that historically hover around 20–25%. However, in Lynch syndrome cohorts under expert surveillance with rigorous procedural quality benchmarks, AI assistance did not significantly improve overall adenoma or advanced neoplasia detection rates. The authors conclude that AI can be safely integrated into Lynch syndrome surveillance programs, but its incremental benefit above expert-level colonoscopy remains undemonstrated.
This finding deserves careful interpretation rather than dismissal. The lack of detectable AI benefit in Lynch syndrome may reflect a ceiling effect: when expert colonoscopists operate under optimized conditions — adequate withdrawal times, bowel preparation standards, and chromoendoscopy — additional algorithmic detection may offer diminishing returns. It also raises a subtler point about generalizability: AI tools trained predominantly on average-risk populations may underperform when confronted with the flat, subtle morphology characteristic of Lynch syndrome lesions. Clinically, this review serves as a timely corrective against assuming that AI benefits demonstrated in broad screening populations automatically transfer to high-risk hereditary subgroups. Future trials enrolling Lynch syndrome patients specifically, and testing AI tools calibrated for non-polypoid lesions, will be necessary before firm recommendations can be made.