Through computer systems, applications belonging to artificial intelligence (AI) and its subsets like machine learning (ML) and deep learning, which can be defined as software-level modeling, have the capability to process large datasets. In the field of orthopedics and traumatology surgery, the use of AI enables functional improvement and reduction of workload through administrative and clinical decision support applications such as cost analysis, discharge time estimation, patient selection, surgical planning, and patient monitoring. To assist in the clinical decision-making process, there are three main types of ML methods: natural language processing, clinical prediction, and outcome calculators that include diagnostic and predictive result applications. The integration of ML into the clinical decision-making process has made it a powerful predictive tool with the ability to process large amounts of information and identify complex patterns. While these applications aim to improve the treatment of patients in the field of orthopedics and traumatology, make hospital management more efficient, and enhance treatment outcomes, it should be noted that they are susceptible to certain biases such as non-random missing data, limited sample size, misclassification of diseases, and discrepancies in measurements. Additionally, the compatibility of the data obtained from algorithms derived from these models with data from different populations or strata, as well as with clinical experiences, should be questioned.