## 1 Introduction  
Breast cancer stands out as the most common type of cancer in Saudi Arabian women, with an annual incidence rate of 14.8% and a mortality rate of 8.5% [1]. According to Saudi Cancer Registry (SCR) statistics, the incidence rate of breast cancer among women in Saudi Arabia has been increasing, accounting for 28.7% of all reported cancers among women of all ages [2]. Early and accurate diagnosis of breast cancer could improve patient survival and clinical outcomes [2]. Thus, appropriate screening methods are important for detecting the early signs of breast cancer [3]. Mammography is one of the most important imaging modalities for the early diagnosis of breast cancer [3]. Human-based diagnosis of breast cancer using mammography screening detection has drawbacks such as a high false-positive rate, overdiagnosis, and overtreatment, which are costly and have negative psychological impacts on patients [4; 5; 6]. In addition, it can lead to a missed diagnosis rate of 15%-35% for breast cancers that are not detectable on breast imaging [7]. Computer-aided detection (CAD) systems have been widely used in clinical practice to assist radiologists in identifying potential breast cancers by mammography [8]. However, CAD does not improve diagnostic accuracy in mammography for breast cancer detection or increase biopsy recommendations [8; 9]. In addition, more research is needed to address the current obstacles to the development of breast cancer CAD systems [10]. Therefore, researchers have developed artificial intelligence (AI) algorithms for the early detection of breast cancer and to improve the interpretation of mammograms for breast cancer screening by reducing false-positive results [11; 12; 13; 14; 15; 16; 17; 18; 19]. AI techniques for cancer imaging in health care have recently been investigated, developed, and evaluated as support technologies for disease detection, prognosis, and clinical decision making [8; 11; 20]. This has enhanced the accuracy and efficiency of breast cancer detection, assisting radiologists with breast cancer screenings and reducing the workload of second readers [14; 17; 18; 21; 22; 23]. Current evidence has highlighted that the use of AI-based tools has improved radiologists' breast cancer detection by mammography without the additional reading time required [17; 24]. To the best of our knowledge, previously, there were limited studies that investigated the potential role of an AI system in detecting breast cancer on mammography among Saudi women [23; 25; 26]. Most of these published studies use different image processing techniques to improve the diagnostic performance of the computerized breast cancer detection methods and classification of breast lesions [23; 25; 26]. In a pilot study using a digital mammographic breast cancer database from Saudi Arabia, the machine learning (ML) method based on a CAD system was used for tumor segmentation [23]. Their proposed technique needs to use the radiologist's knowledge to draw the tumor borders and to have a more accurate and efficient classification process [23]. Similarly, in other studies performed on datasets from Qassim province, Saudi Arabia, their proposed methods used several pre-processing steps to achieve better performance of their proposed methods [25; 26]. However, in our study, we proposed to evaluate the benefits of using automated AI-based tools for digital mammographic imaging in the detection of breast cancer in terms of diagnostic performance and efficiency. AI software has been used in radiology to improve the diagnostic efficiency of radiologists. This software can be used as a decision support tool to help radiologists confirm the incidence of breast cancer early in women and avoid errors while interpreting the signs of breast cancer in images. The results of this study will contribute to achieving the goals of the Kingdom's Vision 2030 for improving the quality and efficiency of health care services [27].