Abstract Background It is known that an esophagus with multiple Lugol-voiding lesions (LVLs) after iodine staining is high risk for esophageal cancer; however, it is preferable to identify high-risk cases without staining because iodine causes discomfort and prolongs examination times. This study assessed the capability of an artificial intelligence (AI) system to predict multiple LVLs from images that had not been stained with iodine as well as patients at high risk for esophageal cancer. Methods We constructed the AI system by preparing a training set of 6634 images from white-light and narrow-band imaging in 595 patients before they underwent endoscopic examination with iodine staining. Diagnostic performance was evaluated on an independent validation dataset (667 images from 72 patients) and compared with that of 10 experienced endoscopists. Results The sensitivity, specificity, and accuracy of the AI system to predict multiple LVLs were 84.4 %, 70.0 %, and 76.4 %, respectively, compared with 46.9 %, 77.5 %, and 63.9 %, respectively, for the endoscopists. The AI system had significantly higher sensitivity than 9/10 experienced endoscopists. We also identified six endoscopic findings that were significantly more frequent in patients with multiple LVLs; however, the AI system had greater sensitivity than these findings for the prediction of multiple LVLs. Moreover, patients with AI-predicted multiple LVLs had significantly more cancers in the esophagus and head and neck than patients without predicted multiple LVLs. Conclusion The AI system could predict multiple LVLs with high sensitivity from images without iodine staining. The system could enable endoscopists to apply iodine staining more judiciously.