LAUSR

Machine learning (ML) has the potential to revolutionize health care and diabetes care. One of the key benefits of using ML in diabetes care is its flexibility and scalability. This makes it suitable for a wide range of tasks, such as identifying patient risk levels, making diagnoses, and predicting outcomes. In addition, ML algorithms are capable of analyzing various types of data, including demographic information, laboratory results, imaging studies, and physician notes, and integrating that information to make predictions about disease risk, diagnosis, treatment options, and prognosis.1,2 However, its limited adoption in clinical care suggests that current strategies are deficient. Currently, ML solutions are often being developed in silos with a focus on insufficient studies of important aspects from development to validation, implementation, and clinical relevance of the models.3 Good Machine Learning Practice (GMLP) includes aspects such as: