## 4 Discussion
investigated the performance of radiologists with and without the support of an AI system and found statistically significant higher AUC values for readings with AI support (0.89) than for unaided readings (0.87) [42]. The same group published a subsequent study to examine the stand-alone performance of an AI system compared with that of 101 radiologists who evaluated nine different cohorts of digital mammogram examinations from four different manufacturers. The results showed that the average AUC was 0.814 for the radiologists and 0.840 for the AI system so that the AI system outperformed 61.4% of the radiologists in terms of AUC [24]. Important results were achieved by [PERSON] et al. (2020) who demonstrated an improvement in the average AUC across 14 radiologists with and without the MammoScreen AI system, with AUC values of 0.769 and 0.797, respectively. The average difference in AUC was 0.028 based on the investigation research [17]. These results suggest that the AI system had better performance in detecting breast cancer than radiological assessment. The results of the present study demonstrate a false-positive rate of 8.7% for the AI system and 26.2% for the radiologists. On the basis of these results, the AI system agrees with the ground truth results confirmed by biopsy or follow-up imaging which achieved high specificity and accuracy for detecting breast cancer and had fewer callbacks for benign findings. These findings are consistent with those of previous studies that demonstrated the high accuracy of the AI system in breast cancer detection [17; 18; 19; 24; 43]. Fewer false-positive markings on mammograms were also found when the AI system was used [17; 19; 44]. In the United States and Europe, the performance of the AI system exceeded that of a single radiologist with regard to sensitivity (56% vs. 48%) and specificity (84% vs. 81%) [18]. Like other population-based studies, our study included all types of mammograms, including those of normal breasts, benign lesions, and breast cancer cases, to obtain optimal data as similar as possible to those obtained from routine screening. By enhancing diagnostic accuracy, this AI program would enable more women to obtain a benign diagnosis without unnecessary callbacks, imaging, or biopsies [17; 19; 21; 44]. This could also help physicians read images with a high degree of confidence and less time interpreting normal mammograms; thereby, patients could receive their results faster. The AI system used in this study had a cutoff suspicion score of \(\leq\)6 (AUROC = 0.967; sensitivity, 92.8% and specificity, 91.9%) which reflects its high accuracy in detecting breast cancer (see Supplementary Results, Figure S1 and Table S1). These results are similar to the those of an American study presented of the Society of Breast Imaging (SBI) in 2021 [45]. MammoScreen was used to analyze 16,004 screening mammograms in a recent US study presented at the 2021 SBI conference and the results revealed that the MammoScreen cutoff suspicion score was 6, the middle point of the MammoScreen lesion suspicion score and the best compromise in terms of sensitivity and specificity. By using the MammoScreen AI system, 27% of breast cancer cases were found 1 year earlier and 21% were found 2 years earlier, with a minimal increase in recall rate [45]. However, further longitudinal research studies are needed to determine the usefulness of the MammoScreen AI system for detecting early-stage breast cancer in the Saudi women cohort. One limitation of the present study is the limited follow-up time of the patients for defining the ground truth, which may provide no false-negative results for patients with normal breast findings. As for normal cases, this provided high specificity to the radiologists' performance in the present study. Further studies are needed with a longer follow-up period of 18 months to allow the detection of intervals and flag cancer cases earlier using the AI program.