The article emphasizes the importance of calibration in risk prediction models, which is often overlooked despite its critical role in clinical decision-making. Poorly calibrated algorithms can lead to misleading and potentially harmful predictions, as they may systematically overestimate or underestimate risks. The authors argue that calibration should be a primary focus in the development and validation of these models. They outline methods to avoid poor calibration, such as controlling for statistical overfitting and ensuring sufficient sample sizes, and suggest strategies for updating models when necessary. The article also provides detailed guidance on assessing calibration, including the use of calibration curves and the Hosmer–Lemeshow test. The ultimate goal is to optimize the utility of predictive analytics for shared decision-making and patient counseling.The article emphasizes the importance of calibration in risk prediction models, which is often overlooked despite its critical role in clinical decision-making. Poorly calibrated algorithms can lead to misleading and potentially harmful predictions, as they may systematically overestimate or underestimate risks. The authors argue that calibration should be a primary focus in the development and validation of these models. They outline methods to avoid poor calibration, such as controlling for statistical overfitting and ensuring sufficient sample sizes, and suggest strategies for updating models when necessary. The article also provides detailed guidance on assessing calibration, including the use of calibration curves and the Hosmer–Lemeshow test. The ultimate goal is to optimize the utility of predictive analytics for shared decision-making and patient counseling.