The discussion elaborates on the challenges and potential of machine learning (ML) models in healthcare, particularly in recognizing critical health declines. Comments from industry professionals underscore the importance of employing reliable methods like extended Kalman filters instead of black-box ML techniques. There is a specific reference to the Artera AI test, which assesses prostate cancer treatment plans and its cost-effectiveness. While AI can enhance medical decision-making, the reliance on high-quality training data and the necessity for human oversight is emphasized, especially given the complexities of patient care.