The authors argue that the accuracy of medical AI should not be confused with its efficiency. While AI systems have shown promising results in terms of accuracy, particularly in time-consuming or strenuous tasks for healthcare professionals, this does not necessarily translate to increased efficiency. The development and validation of AI systems require significant human labor and time, and ongoing support is needed even after integration into daily workflows. Additionally, AI systems are not perfect and can make mistakes, which may lead to biased outcomes or system failures. These issues can undermine efficiency and patient safety. Furthermore, the human aspect of technology implementation, including the knowledge, competencies, and trust of healthcare professionals, plays a crucial role in how AI systems are used and their overall efficiency. The authors conclude that it is important to remain critical and conscious about the expected benefits of AI, especially when discussing systemic changes based on single studies. They emphasize the need for clear distinctions between effectiveness and efficiency, and more research on the relationship between trust and efficiency in AI systems.The authors argue that the accuracy of medical AI should not be confused with its efficiency. While AI systems have shown promising results in terms of accuracy, particularly in time-consuming or strenuous tasks for healthcare professionals, this does not necessarily translate to increased efficiency. The development and validation of AI systems require significant human labor and time, and ongoing support is needed even after integration into daily workflows. Additionally, AI systems are not perfect and can make mistakes, which may lead to biased outcomes or system failures. These issues can undermine efficiency and patient safety. Furthermore, the human aspect of technology implementation, including the knowledge, competencies, and trust of healthcare professionals, plays a crucial role in how AI systems are used and their overall efficiency. The authors conclude that it is important to remain critical and conscious about the expected benefits of AI, especially when discussing systemic changes based on single studies. They emphasize the need for clear distinctions between effectiveness and efficiency, and more research on the relationship between trust and efficiency in AI systems.