الفهرس 
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Abstract
B
reast cancer is the second leading cause of death from cancer in women, but early detection and treatment can considerably improve outcomes. As a consequence, many developed nations have implemented large-scale mammography screening programmes.
Despite the widespread adoption of mammography, interpretation of these images remains challenging. The accuracy achieved by experts in cancer detection varies widely, and the performance of even the best clinicians leaves room for improvement.
As a shortage of mammography professionals threatens the availability and adequacy of breast-screening services around the world, However, AI accuracy has been proven to be comparable to the radiologist 1 (highest years of experience) with equal specificity and lower sensitivity, and not inferior and even higher than radiologist 2 and 3 (medium and low years of experience), the reading time of the AI software was much less than the three radiologists. and the highest agreement of the AI software was with the radiologist 1 (the most experienced).
So, AI can be used as a decision supportive tool without wasting time.
However, as promising as these findings may be, studies within a screening scenario should be performed to validate them and seize the real accuracy of AI in screening.