Background: Artificial intelligence (AI) algorithms for the independent assessment of screening mammograms have not been well established in a large screening cohort of Asian women. We compared the performance of screening digital mammography considering breast density, between radiologists and AI standalone detection among Korean women. Methods: We retrospectively included 89,855 Korean women who underwent their initial screening digital mammography from 2009 to 2020. Breast cancer within 12 months of the screening mammography was the reference standard, according to the National Cancer Registry. Lunit software was used to determine the probability of malignancy scores, with a cutoff of 10% for breast cancer detection. The AI’s performance was compared with that of the final Breast Imaging Reporting and Data System category, as recorded by breast radiologists. Breast density was classified into four categories (A–D) based on the radiologist and AI-based assessments. The performance metrics (cancer detection rate [CDR], sensitivity, specificity, positive predictive value [PPV], recall rate, and area under the receiver operating characteristic curve [AUC]) were compared across breast density categories. Results: Mean participant age was 43.5 ± 8.7 years; 143 breast cancer cases were identified within 12 months. The CDRs (1.1/1000 examination) and sensitivity values showed no significant differences between radiologist and AI-based results (69.9% [95% confidence interval [CI], 61.7–77.3] vs. 67.1% [95% CI, 58.8–74.8]). However, the AI algorithm showed better specificity (93.0% [95% CI, 92.9–93.2] vs. 77.6% [95% CI, 61.7–77.9]), PPV (1.5% [95% CI, 1.2–1.9] vs. 0.5% [95% CI, 0.4–0.6]), recall rate (7.1% [95% CI, 6.9–7.2] vs. 22.5% [95% CI, 22.2–22.7]), and AUC values (0.8 [95% CI, 0.76–0.84] vs. 0.74 [95% CI, 0.7–0.78]) (all P < 0.05). Radiologist and AI-based results showed the best performance in the non-dense category; the CDR and sensitivity were higher for radiologists in the heterogeneously dense category (P = 0.059). However, the specificity, PPV, and recall rate consistently favored AI-based results across all categories, including the extremely dense category. Conclusions: AI-based software showed slightly lower sensitivity, although the difference was not statistically significant. However, it outperformed radiologists in recall rate, specificity, PPV, and AUC, with disparities most prominent in extremely dense breast tissue.

Early detection of a premetabolic status that is at risk for metabolic syndrome (MetS) but not meeting the criteria is crucial. This study examined 27,623 participants aged 20–50 (mean: 40.7) years who underwent initial health screening at Kangbuk Samsung Hospital (2011–2019), focusing on individuals with one or two MetS components. Hierarchical agglomerative clustering was used to form MetS risk clusters based on initial and follow-up data, including age, resting heart rate (rHR), serum uric acid (UA), C-reactive protein (CRP), gamma-glutamyl transpeptidase, and ferritin levels, and nonalcoholic fatty liver disease (NAFLD), periodontal disease, and Helicobacter pylori infection duration. Kaplan–Meier and generalized additive models were used to present the restricted mean survival time (RMST) and identify onset contributors. Clusters with NAFLD and elevated UA levels had the highest MetS risk, whereas those with uniformly low biomarker levels revealed the lowest risk. During follow-up, a cluster initially comprising 60.2% moderate-risk patients exhibited high biomarker levels and had the worst MetS prognosis (RMST: 211 days). UA, CRP levels, and rHR contributed to the incidence of MetS in the fitted model. Machine learning can predict the premetabolic state at MetS risk in a population-based cohort.