Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Purpose: This study evaluated the educational impact of an artificial intelligence (AI)–based decision support system for breast ultrasonography (US) on medical professionals not specialized in breast imaging. Methods: In this multi-case, multi-reader study, educational materials, including American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) descriptors, were provided alongside corresponding AI results during training. The AI system presented results in the form of AIheatmaps, AI scores, and AI-provided BI-RADS assessment categories. Forty-two readers evaluated the test set in three sessions: the first session (S1) occurred before the educational intervention, the second session (S2) followed education without AI assistance, and the third session (S3) took place after education with AI assistance. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and overall performance, were compared between the sessions. Results: The mean sensitivity increased from 66.5% (95% confidence interval [CI], 59.2% to 73.7%) to 88.7% (95% CI, 84.1% to 93.3%), with a statistically significant difference (P<0.001), and the AUC non-significantly increased from 0.664 (95% CI, 0.606 to 0.723) to 0.684 (95% CI, 0.620 to 0.748) (P=0.300). Both measures were higher in S2 than in S1. The AI-achieved AUC was comparable to that of the expert reader (0.747 [95% CI, 0.640 to 0.855] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.217). Additionally, with AI assistance, the mean AUC for inexperienced readers was not significantly different from that of the expert reader (0.745 [95% CI, 0.660 to 0.830] vs. 0.803 [95% CI, 0.706 to 0.900], P=0.120). Conclusion The mean AUC and sensitivity improved after incorporating AI into breast US education and interpretation. AI systems with high-level performance for breast US can potentially be used as educational tools in the interpretation of breast US images.

Purpose: This study aimed to identify the relationship between nursing competency, professional self-concept, and job satisfaction of outpatient oncology nurses caring for cancer patients and to identify the influencing factors on job satisfaction. Methods: This study was a cross-sectional study to determine the relationship between nursing competency, professional self-concept, and job satisfaction of outpatient oncology nurses. Data were collected from 104 outpatient oncology nurses at a tertiary hospital in Seoul, South Korea, using a self-report questionnaire. Descriptive statistics, Pearson correlations, and multiple regression analyses were conducted using SPSSWIN 27.0. Results: The results showed that the nursing competency mean was 3.89±0.46 out of 5, professional self-concept mean was 2.84±0.36 out of 4, and job satisfaction mean was 3.88±0.57 out of 5. Job satisfaction was significantly positively correlated with nursing competency (r=.70, p<.001) and professional self-concept (r=.63, p<.001). Multiple regression analysis revealed that nursing competency (β=.51, p<.001) and professional self-concept (β= .31, p=.001) were significant predictors of job satisfaction and had an overall explanatory power of 54%. Conclusion In this study, professional self-concept and nursing competency were identified as influential factors in the job satisfaction of outpatient oncology nurses caring for cancer patients.Based on the findings of this study, it is necessary to develop a program to increase professional self-concept and enhance nursing competency to improve the job satisfaction of outpatient oncology nurses.

Background: This study was designed to determine the current status of diagnosis and treatment of valvular heart disease (VHD) in Korea. Methods: A nationwide registry study was conducted in 45 hospitals in Korea involving adult patients with at least moderate VHD as determined by echocardiography carried out between September and October of 2019. Of a total of 4,094 patients with at least moderate VHD, 1,482 had severe VHD (age, 71.3 ± 13.5 years; 49.1% male). Echocar‑ diographic data used for the diagnosis of each case of VHD were analyzed. Experts from each center determined the diagnosis and treatment strategy for VHD based on current guidelines and institutional policy. The clinical out‑ come was in-hospital mortality. Results: Each valve underwent surgical or transcatheter intervention in 19.3% cases of severe mitral stenosis, 31.4% cases of severe primary mitral regurgitation (MR), 7.5% cases of severe secondary MR, 43.7% cases of severe aortic stenosis, 27.5% cases of severe aortic regurgitation, and 7.2% cases of severe tricuspid regurgitation. The overall inhospital mortality rate for patients with severe VHD was 5.4%, and for secondary severe MR and severe tricuspid regur‑ gitation, the rates were 9.0% and 7.5%, respectively, indicating a poor prognosis. In-hospital mortality occurred in 73 of the 1,244 patients (5.9%) who received conservative treatment and in 18 of the 455 patients (4.0%) who received a surgical or transcatheter intervention, which was significantly lower in the intervention group (P = 0.037). Conclusions This study provides important information about the current status of VHD diagnosis and treatment through a nationwide registry in Korea and helps to define future changes.

Purpose: This study aimed to identify the relationship between nursing competency, professional self-concept, and job satisfaction of outpatient oncology nurses caring for cancer patients and to identify the influencing factors on job satisfaction. Methods: This study was a cross-sectional study to determine the relationship between nursing competency, professional self-concept, and job satisfaction of outpatient oncology nurses. Data were collected from 104 outpatient oncology nurses at a tertiary hospital in Seoul, South Korea, using a self-report questionnaire. Descriptive statistics, Pearson correlations, and multiple regression analyses were conducted using SPSSWIN 27.0. Results: The results showed that the nursing competency mean was 3.89±0.46 out of 5, professional self-concept mean was 2.84±0.36 out of 4, and job satisfaction mean was 3.88±0.57 out of 5. Job satisfaction was significantly positively correlated with nursing competency (r=.70, p<.001) and professional self-concept (r=.63, p<.001). Multiple regression analysis revealed that nursing competency (β=.51, p<.001) and professional self-concept (β= .31, p=.001) were significant predictors of job satisfaction and had an overall explanatory power of 54%. Conclusion In this study, professional self-concept and nursing competency were identified as influential factors in the job satisfaction of outpatient oncology nurses caring for cancer patients.Based on the findings of this study, it is necessary to develop a program to increase professional self-concept and enhance nursing competency to improve the job satisfaction of outpatient oncology nurses.