![]() Exactly, AI predictive models can bring objective results after learning input features and countless calculations. ![]() ![]() If there is an objective tool at hand to predict the prognosis according to the patient’s condition quickly and accurately, using this tool to corroborate your judgment will make you more confident in judging the prognosis of your patient. ![]() Imagine that you are a young inexperienced physician and you are on duty in the ward, your patient asks you about the outcomes after treatment and you cannot ensure the judgment is right based on your own experience. In clinical, patients are most concerned about their clinical outcomes. In addition, predicting the outcome of a stroke often depends on the experience of the physician clinically, but it is difficult for inexperienced young physicians to judge the prognosis. There were some studies suggesting that ML and DL have again recently achieved substantial improvements and demonstrated comparable performance to trained physicians in the fields of other departments, like radiology and dermatology ( Gulshan et al., 2016 Esteva et al., 2017 Hannun et al., 2019).Īcute stroke ranks among the leading causes of morbidity and mortality worldwide, and it can be divided into ischemic stroke and hemorrhagic stroke ( Toyoda et al., 2022). The advent of neural networks (NN) and deep learning (DL) techniques has changed the ML domain and achieved automatic and efficient feature recognition and processing in covert analysis networks without prior feature selection. ML techniques utilize various methods for automated data analysis, including logistic regression (LR), random forests (RF), support vector machines (SVM), and classification trees, which allow combining features (data characteristics) with flexible decision boundaries in a non-linear manner. Machine learning (ML) is one way to implement AI, which has shown the greatest potential in dealing with problems involving unstructured data, such as image recognition ( Deo, 2015 Esteva et al., 2019). ![]() Artificial intelligence (AI) can be defined as the ability of computers or other machines to demonstrate or simulate intelligent behavior, like human beings ( Krittanawong et al., 2017 Garcia-Vidal et al., 2019 Schwalbe and Wahl, 2020 Bonkhoff and Grefkes, 2022). ![]()
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