AI is beginning to move beyond experimental settings and into real-world clinical practice. Early evidence suggests that it can enhance both accuracy and efficiency. At the same time, important challenges remain, including risks of bias, overdiagnosis, and uneven performance across clinical environments. However, sustainable progress will require robust validation, careful integration into clinical workflows, and policies that ensure the technology benefits patients across diverse settings.
Yazarkan et al. (Mon,) studied this question.