Purpose: 11C-Methionine PET/CT is a promising method for detecting parathyroid lesions in patients with primary hyperparathyroidism (PHPT). We aimed to determine the diagnostic ability and correlation of digital 11C-Methionine PET/CT for parathyroid lesions in patients with PHPT, particularly in cases where standard imaging methods yielded inconclusive results. Methods: This retrospective analysis was conducted on patients diagnosed with PHPT who underwent digital 11C-Methionine PET/CT imaging because of ambiguous results on standard imaging work-up ( 99m Tc-MIBI parathyroid scan and/or neck ultrasonography). Quantitative 11C-Methionine PET/CT parameters, including maximum standardized uptake value (SUVmax), mean SUV (SUVmean), peak SUV (SUVpeak), parathyroid methionine volume (PMV), and whole methionine uptake(WMU: PMV multiplied by SUVmean) were calculated with various thresholds, and their correlations with biochemical andpathologic parameters were investigated. Results: This study included 22 consecutive patients (10 men and 12 women) with a median age of 64.0 years. The lesion detection rate and sensitivity of digital 11C-Methionine PET/CT were 81.8% (18/22) and 100.0% (18/18), respectively.Quantitative analysis revealed that serum PTH (r = 0.490, P = 0.039) and serum calcium (r = 0.583, P = 0.011) were signifi-cantly correlated with PMV50%. Conclusion Digital 11C-Methionine PET/CT offers good performance in the detection of parathyroid lesions in PHPT patients with inconclusive standard imaging work-up. The volume parameter of PMV50% significantly correlated biochemi-cal parameters and can serve as a complementary diagnostic tool.

Endometriosis, a prevalent but debilitating condition affecting women, poses significant challenges in diagnosis and management. The current 2024 guideline, developed by the Korean Society of Endometriosis (KSE), builds upon the 2018 KSE guideline. This guideline aims to provide customized recommendations tailored to Korea’s unique clinical aspects and medical environment, and addresses key areas such as diagnosis, medical and surgical management, considerations for special populations, and its complex relationship with cancer.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Purpose: 11C-Methionine PET/CT is a promising method for detecting parathyroid lesions in patients with primary hyperparathyroidism (PHPT). We aimed to determine the diagnostic ability and correlation of digital 11C-Methionine PET/CT for parathyroid lesions in patients with PHPT, particularly in cases where standard imaging methods yielded inconclusive results. Methods: This retrospective analysis was conducted on patients diagnosed with PHPT who underwent digital 11C-Methionine PET/CT imaging because of ambiguous results on standard imaging work-up ( 99m Tc-MIBI parathyroid scan and/or neck ultrasonography). Quantitative 11C-Methionine PET/CT parameters, including maximum standardized uptake value (SUVmax), mean SUV (SUVmean), peak SUV (SUVpeak), parathyroid methionine volume (PMV), and whole methionine uptake(WMU: PMV multiplied by SUVmean) were calculated with various thresholds, and their correlations with biochemical andpathologic parameters were investigated. Results: This study included 22 consecutive patients (10 men and 12 women) with a median age of 64.0 years. The lesion detection rate and sensitivity of digital 11C-Methionine PET/CT were 81.8% (18/22) and 100.0% (18/18), respectively.Quantitative analysis revealed that serum PTH (r = 0.490, P = 0.039) and serum calcium (r = 0.583, P = 0.011) were signifi-cantly correlated with PMV50%. Conclusion Digital 11C-Methionine PET/CT offers good performance in the detection of parathyroid lesions in PHPT patients with inconclusive standard imaging work-up. The volume parameter of PMV50% significantly correlated biochemi-cal parameters and can serve as a complementary diagnostic tool.

Endometriosis, a prevalent but debilitating condition affecting women, poses significant challenges in diagnosis and management. The current 2024 guideline, developed by the Korean Society of Endometriosis (KSE), builds upon the 2018 KSE guideline. This guideline aims to provide customized recommendations tailored to Korea’s unique clinical aspects and medical environment, and addresses key areas such as diagnosis, medical and surgical management, considerations for special populations, and its complex relationship with cancer.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

South Korea’s current vaccination policies leave a surveillance gap for non-National Immunization Program (NIP) vaccines. In this study, we proposed a sentinel surveillance approach for monitoring the safety of non-NIP vaccines. Vaccination data were collected retrospectively among patients hospitalized with pre-defined adverse events of special interest (AESI) by reviewing electronic medical records in five university hospitals. This approach incorporates expert assessment to determine the causal relationship. We confirmed that 16 patients had received non-NIP vaccines among 860 patients diagnosed with AESI.We concluded one case of preeclampsia was possibly related to tetanus-diphtheria-pertussis vaccination. We propose a multi-hospital-based, retrospective assessment system for predefined AESIs as an alternative to active vaccine safety monitoring method. These efforts are expected to enhance both the accuracy and timeliness of safety monitoring in South Korea.

Purpose: This study aimed to develop a question-answering chatbot that provides accurate and consistent answers to questions that may arise during the recovery process of patients with coronary artery disease after percutaneous coronary intervention, and to evaluate the chatbot. Methods: The chatbot was developed through the stages of analysis, design, implementation, and evaluation. It was evaluated by five experts, and the user experience was evaluated by 27 patients who underwent percutaneous coronary intervention. Furthermore, chatbot utilization was analyzed based on user experience logs. Results: The chatbot was constructed as a question-answering database that included three categories: coronary artery disease, percutaneous coronary intervention, and post-intervention management. The question-answering chatbot, referred to as the “Cardiovascular Strong” channel, has been launched and implemented. An expert evaluation of the chatbot revealed no usability issues or necessary modifications. The overall result of the user experience evaluation was 4.26 points. Based on the user experience log, the question-answer accuracy was 84.6%, and medications during post-intervention management were the most frequently searched topic, accounting for 110 cases (20.8%) out of a total of 528. Conclusion The chatbot that was developed to provide information via real-time answers to questions after the intervention can be easily accessed in clinical settings with no time or space constraints. It also will contribute to providing accurate disease-related information via the familiar KakaoTalk platform.