Artificial Intelligence in Colonoscopy Surveillance for Lynch Syndrome: Emerging Evidence, Lessons Learned From Average‐Risk Populations, and Future Directions

Lynch syndrome (LS) is the most common hereditary colorectal cancer (CRC) syndrome and is characterized by an accelerated adenoma‐carcinoma sequence, a relatively higher prevalence of flat and subtle CRC precursor lesions, and exceptionally high adenoma miss rates despite intensive colonoscopy surveillance. Artificial intelligence (AI), particularly through computer‐aided detection (CADe), has demonstrated substantial improvements in adenoma detection in average‐risk CRC screening and surveillance populations. Meanwhile, it is unclear whether these benefits also translate to LS, where carcinogenesis, surveillance regimens, and clinical standards differ fundamentally. This narrative review synthesizes the current evidence on AI‐assisted colonoscopy in LS, including findings from the randomized controlled CADLY and TIMELY trials. We contextualize these results within the broader body of research on AI‐assisted colonoscopy in average‐risk CRC screening and surveillance populations. Existing LS‐specific data suggest that AI can be safely integrated into high‐quality surveillance. Meanwhile, use of AI has not yet been demonstrated to aid in improving overall adenoma or advanced neoplasia detection rates when used by expert colonoscopists, and when adequate baseline procedural quality is guaranteed.