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Objective: To develop and externally validate an updated artificial intelligence (AI) prediction system for stratifying the risk of lymph node metastasis (LNM) in T2 colorectal cancer (CRC). Background: Recent technical advances allow complete local excision of T2 CRC, traditionally treated with surgical resection. Yet, the widespread adoption of this approach is hampered by the inability to stratify the risk of LNM. Methods: Data from patients with pT2 CRC undergoing surgical resection between April 2000 and May 2022 at one Japanese and one Italian center were analyzed. Primary goal was AI system development for accurate LNM prediction. Predictors encompassed 7 variables: age, sex, tumor size, tumor location, lymphovascular invasion, histologic differentiation, and carcinoembryonic antigen level. The tool's discriminating power was assessed through area under the curve, sensitivity, and specificity. Results: Out of 735 initial patients, 692 were eligible. Training and validation cohorts comprised of 492 and 200 patients, respectively. The AI model displayed an area under the curve of 0.75 in the combined validation data set. Sensitivity for LNM prediction was 97.8%, and specificity was 15.6%. The positive and the negative predictive value were 25.7% and 96%, respectively. The false negative rate was 2.2%, and the false positive was 84.4%. Conclusions: Our AI model, based on easily accessible clinical and pathologic variables, moderately predicts LNM in T2 CRC. However, the risk of false negative needs to be considered. The training of the model including more patients across western and eastern centers -- differentiating between colon and rectal cancers -- may improve its performance and accuracy. [ABSTRACT FROM AUTHOR]