The 2020-2023 study focused on creating an artificial intelligence model to predict neurological complications after surgery in patients with acute type A aortic dissection. The research included 853 patients to train the model and 375 patients to verify its accuracy. Neurological complications occurred in 237 patients (27.8%), including 203 with temporary and 34 with permanent disorders. The researchers tested four different machine learning algorithms and selected 15 key predictive factors from the original 49 data sets. The XGBoost model achieved the best results with an accuracy of 96.6% in internal testing and 95.1% in external validation, outperforming traditional statistical methods. The algorithm allows doctors to identify patients at high risk of neurological complications and provide them with early treatment.