{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T13:23:31Z","timestamp":1787664211389,"version":"build-2736575974"},"reference-count":106,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T00:00:00Z","timestamp":1787616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Clin. Med.","JCM"],"abstract":"<jats:p>Background: Colonoscopy is the gold-standard screening modality for colorectal cancer (CRC) prevention, enabling detection and endoscopic resection of premalignant polyps and reducing CRC incidence and mortality by up to 77% and 53%, respectively. However, colonoscopy effectiveness is substantially dependent on endoscopist expertise, with significant inter-operator variability in adenoma detection rates (ADR) and, consequently, a risk of missed lesions, particularly diminutive and morphologically subtle adenomas. Recent advances in artificial intelligence (AI), specifically computer-aided detection (CADe) and computer-aided diagnosis (CADx) systems utilizing deep learning convolutional neural networks, have emerged as promising technologies to standardize lesion detection accuracy and reduce adenoma miss rates. Methods: A focused narrative literature review was conducted examining randomized controlled trials, meta-analyses, and implementation studies evaluating AI-assisted colonoscopy systems across diverse clinical populations and healthcare settings. Results: Evidence demonstrates that CADe systems consistently improve ADR, particularly for diminutive polyps and morphologically challenging lesions, though superiority over expert endoscopists remains inconsistent. CADx systems reliably meet ASGE-PIVI performance thresholds for diminutive polyp characterization, supporting implementation of resect-and-discard and diagnose-and-leave strategies. However, substantial heterogeneity exists regarding real-world effectiveness, cost-effectiveness, and optimal implementation frameworks across diverse settings. Conclusions: While AI-assisted colonoscopy demonstrates clinical promise in improving lesion detection and enabling optical diagnosis, realizing durable population-level benefit requires the establishment of standardized validation methodologies, large-scale pragmatic trials with patient-centered outcomes, robust regulatory frameworks, and equitable implementation strategies addressing health disparities globally.<\/jats:p>","DOI":"10.3390\/jcm15176558","type":"journal-article","created":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T12:57:17Z","timestamp":1787662637000},"page":"6558","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions"],"prefix":"10.3390","volume":"15","author":[{"given":"Andreas","family":"Antzoulas","sequence":"first","affiliation":[{"name":"Department of Surgery, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7198-2628","authenticated-orcid":false,"given":"Francesk","family":"Mulita","sequence":"additional","affiliation":[{"name":"Second Department of Surgery, Medical School, Democritus University of Thrace, 68100 Alexandroupolis, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8692-0059","authenticated-orcid":false,"given":"Vasileios","family":"Leivaditis","sequence":"additional","affiliation":[{"name":"Department of Cardiothoracic and Vascular Surgery, Westpfalz Klinikum, 67655 Kaiserslautern, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elias","family":"Liolis","sequence":"additional","affiliation":[{"name":"Department of Oncology, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-0565-5408","authenticated-orcid":false,"given":"Platon","family":"Dimopoulos","sequence":"additional","affiliation":[{"name":"Department of Radiology, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9613-4681","authenticated-orcid":false,"given":"Vasiliki","family":"Tzelepi","sequence":"additional","affiliation":[{"name":"Department of Pathology, Patras University Hospital, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8988-8124","authenticated-orcid":false,"given":"Ioannis","family":"Maroulis","sequence":"additional","affiliation":[{"name":"Department of Surgery, University of Patras, 26504 Patras, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4496-275X","authenticated-orcid":false,"given":"Christos-Nikolaos","family":"Anagnostopoulos","sequence":"additional","affiliation":[{"name":"Department of Cultural Technology and Communications, University of the Aegean, 81100 Mitilini, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,25]]},"reference":[{"key":"ref_1","first-page":"e70043","article-title":"Cancer statistics, 2026","volume":"76","author":"Siegel","year":"2026","journal-title":"CA A Cancer J. 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