## 4 Discussion  
AI applications play a significant role in onoradiology in terms of the early detection and precise diagnosis of breast cancer. Early cancer detection increases the likelihood of effective treatment in different types of malignancy [31]. Recently, the implementation of digital mammographic screening programs is the most essential method to reduce cancer morbidity and mortality that has led to improvements in patient survival [29]. Owing to \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Variable**} & \multirow{2}{*}{**Categories**} & \multicolumn{3}{c}{**Ground Truth**} \\ \cline{3-5}  & & **Negative** & **Positive** & **Total** \\  & & **_n_ **(\%)** & **_n_ **(\%)** & **_n_ **(\%)** \\ \hline \multirow{2}{*}{AI categories} & Negative & 182 (93.3) & 13 (6.7) & 195 (100) \\ \cline{2-5}  & Positive & 16 (8.7) & 167 (91.3) & 183 (100) \\ \hline \multirow{2}{*}{Radiologists’ assessment (RA)} & Negative & 134 (100) & 0 (0) & 134 (100) \\ \cline{2-5}  & Positive & 64 (26.2) & 180 (73.8) & 244 (100) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Cross tabulation between the AI system and radiologists’ assessment (RA) with ground truth results. Figure 3: (**A**) Receiver operating characteristic (ROC) curves of the radiologists’ and (**B**) artificial intelligence (AI) system assessment results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable** & **Sensitivity** & **Specificity** & **PPV** & **NPV** & **Accuracy** & **AUROC** \\ \hline AI categories & 92.8\% & 91.9\% & 91.3\% & 93.3\% & 92.3\% & 0.923 \\ \hline \multirow{2}{*}{Radiologists’ assessment (RA)} & \multirow{2}{*}{100\%} & \multirow{2}{*}{67.7\%} & \multirow{2}{*}{73.8\%} & \multirow{2}{*}{100\%} & \multirow{2}{*}{83.1\%} & \multirow{2}{*}{0.838} \\  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Accuracy measures for the AI system’s assessments and radiologists’ assessment (RA). the increased interest in AI in radiology applications, various modern deep learning-based algorithms have been created and applied to digital mammography. Preliminary research has shown that the use of AI systems to provide concurrent mammographic interpretations can increase radiological efficiency in terms of time, sensitivity, and specificity [22; 32]. AI systems based on CAD neural network algorithms are useful in the detection of breast lesions and in reducing the false-positive rate. On the other hand, radiologists' performance in mammography screening has a high false-negative rate, especially in cases with dense breast parenchyma [33; 34; 35; 36; 37; 38; 39]. The histopathological test results in the present study show that invasive ductal carcinoma was the most common type of cancer among the patients. This finding is consistent with previously published reports that invasive ductal carcinoma is the most common type of breast cancer among Saudi women, accounting for more than 80% of the most common malignancy lesions [40; 41]. This breast cancer is diagnosed at an average age of \(<\)50 years. This indicates the urgency for the implementation of breast screening programs and an accurate diagnostic system [41]. In our study, a dataset of digital mammograms, including those of different types of breast abnormalities and normal breasts, were identified using the MammoScreen AI system. This AI system is based on CNNs which are the most frequently used neural networks in radiological studies. By observing the overall tendency of all ROC curves, we demonstrated in this study that the AI system outperformed the radiologists in detecting breast cancer. The AI system had a slightly higher AUC value than the radiologists, with a difference of 0.085 (0.923 vs. 0.838). The results of several previous studies are consistent with our results [17; 18; 19; 24; 42; 43]. According to [PERSON] et al., the AUC for the AI system was greater than the average for six radiologists by an absolute margin of 11.5% [20]. Similarly, [PERSON] et al.