TRIPOD provides guidance on the key items to report when describing studies developing, evaluating (or validating), or updating clinical prediction models.10,11 Although TRIPOD aims primarily to improve reporting, it also leads to more comprehensive understanding, conduct, and analysis of prediction model studies, ensuring that prediction models can be picked up by subsequent researchers and users to be studied further and used to guide health care, thus encouraging reproducible research and reduce research waste. ...concerns have been raised that artificial intelligence in clinical medicine is overhyped and, if not used with proper guidance, knowledge, or expertise, has methodological shortcomings, poor transparency, and poor reproducibility.12 Methodological concerns include an often incorrect focus on classification over prediction, overfitting (whereby too many predictors or features are included for the sample size), lack of robust assessment of predictive accuracy when used with other data than those from which they were developed (validation), weak and unbiased comparison with simpler modelling approaches, and lack of transparency of the artificial intelligence and machine learning algorithm, which limits independent evaluation. Clearly, the consequences of making a wrong or inaccurate prediction are substantial for the clinical application of a machine learning prediction model, such as the deep learning models for detection of stroke or wrist fractures approved by the US Food and Drug Administration.13 Therefore, the clinical community must not get mesmerised by the artificial intelligence and machine learning revolution, and artificial intelligence and machine learning prediction models must be appropriately developed, evaluated, and—if needed—tailored to different situations before they are used in daily medical practice.