Objectives: Linked-colour imaging/blue-laser imaging (LCI/BLI) and CAD-EYE artificial intelligence have been developed for sporadic colorectal neoplasia, but their role in ulcerative colitis (UC) surveillance remains uncertain. We evaluated CAD-EYE-assisted detection and optical characterization using conventional and disease-specific classifications in UC. Methods: In this prospective tandem study, patients with UC undergoing surveillance colonoscopy were examined sequentially using white-light imaging (WLI), LCI, and LCI with CAD-EYE. Lesions were characterized using BLI with Kudo, NICE, and Kudo-IBD classifications, followed by CAD-EYE characterization. Detection and characterization performance were assessed using miss rates and diagnostic accuracy. Results: Among 82 patients, 281 lesions, including 22 neoplastic lesions, were identified. The lesion miss rate decreased from 5.7% with WLI to 2.5% with LCI, while no lesions were missed during the CAD-EYE-assisted withdrawal. However, the fixed examination sequence and absence of prospectively recorded withdrawal times precluded isolation of CAD-EYE’s incremental contribution. The neoplasia miss rate was numerically lower with CAD-EYE than with WLI and LCI, although not significantly. Kudo-IBD showed the highest diagnostic performance, with sensitivity, specificity, and positive and negative predictive values of 90.9%, 85.3%, 34.5%, and 99.1%, respectively. Accuracy was significantly higher with KUDO-IBD than with all other methods (all p < 0.001). Conclusions: In this sequential protocol, CAD-EYE-assisted imaging was associated with complete observed lesion detection, whereas Kudo-IBD provided superior optical characterization. These findings support complementary AI-assisted detection and disease-specific optical assessment. Larger studies with independent examination sequences and prospectively recorded withdrawal times, together with external validation of Kudo-IBD, are needed.