Mammography is the standard screening method for breast cancer, proven to reduce mortality. However, its diagnostic performance varies depending on patient characteristics and radiologist expertise. Dense breast tissue, present in approximately 70% of Korean women aged 40 to 59, limits detection by obscuring malignancies. Additionally, optimal interpretation requires extensive training, which is not always achievable. Artificial intelligence-based computer-aided diagnosis (AI-CAD) has emerged as a promising tool for enhancing mammographic accuracy and efficiency.Current Concepts: AI-CAD has shown diagnostic performance comparable to that of experienced radiologists while addressing the limitations of traditional CAD systems, particularly excessive false positives. Studies suggest AI-CAD improves radiologists' accuracy, particularly among those with limited breast imaging experience. In Europe, AI-assisted reading is increasingly recognized as a viable alternative to traditional double reading. In Korea, adoption of AI-CAD is expanding, with systems approved by the Korean Food and Drug Administration currently in clinical use. Recently, one AI-CAD system received conditional non-reimbursement designation, allowing hospitals to use it for up to 5 years while collecting clinical evidence to support future insurance coverage decisions.Discussion and Conclusion: AI-CAD has significant potential to enhance early breast cancer detection while maintaining acceptable false-positive rates, making it a valuable adjunct in screening programs. Beyond improved detection, AI-CAD may optimize workflow efficiency by triaging cases and prioritizing high-risk examinations. However, its integration into clinical practice necessitates standardized guidelines, regulatory oversight, and further validation through large-scale prospective studies. As AI technology continues to advance, ongoing investigation into its role in personalized breast cancer screening is essential.

Mammography is the standard screening method for breast cancer, proven to reduce mortality. However, its diagnostic performance varies depending on patient characteristics and radiologist expertise. Dense breast tissue, present in approximately 70% of Korean women aged 40 to 59, limits detection by obscuring malignancies. Additionally, optimal interpretation requires extensive training, which is not always achievable. Artificial intelligence-based computer-aided diagnosis (AI-CAD) has emerged as a promising tool for enhancing mammographic accuracy and efficiency.Current Concepts: AI-CAD has shown diagnostic performance comparable to that of experienced radiologists while addressing the limitations of traditional CAD systems, particularly excessive false positives. Studies suggest AI-CAD improves radiologists' accuracy, particularly among those with limited breast imaging experience. In Europe, AI-assisted reading is increasingly recognized as a viable alternative to traditional double reading. In Korea, adoption of AI-CAD is expanding, with systems approved by the Korean Food and Drug Administration currently in clinical use. Recently, one AI-CAD system received conditional non-reimbursement designation, allowing hospitals to use it for up to 5 years while collecting clinical evidence to support future insurance coverage decisions.Discussion and Conclusion: AI-CAD has significant potential to enhance early breast cancer detection while maintaining acceptable false-positive rates, making it a valuable adjunct in screening programs. Beyond improved detection, AI-CAD may optimize workflow efficiency by triaging cases and prioritizing high-risk examinations. However, its integration into clinical practice necessitates standardized guidelines, regulatory oversight, and further validation through large-scale prospective studies. As AI technology continues to advance, ongoing investigation into its role in personalized breast cancer screening is essential.

Mammography is the standard screening method for breast cancer, proven to reduce mortality. However, its diagnostic performance varies depending on patient characteristics and radiologist expertise. Dense breast tissue, present in approximately 70% of Korean women aged 40 to 59, limits detection by obscuring malignancies. Additionally, optimal interpretation requires extensive training, which is not always achievable. Artificial intelligence-based computer-aided diagnosis (AI-CAD) has emerged as a promising tool for enhancing mammographic accuracy and efficiency.Current Concepts: AI-CAD has shown diagnostic performance comparable to that of experienced radiologists while addressing the limitations of traditional CAD systems, particularly excessive false positives. Studies suggest AI-CAD improves radiologists' accuracy, particularly among those with limited breast imaging experience. In Europe, AI-assisted reading is increasingly recognized as a viable alternative to traditional double reading. In Korea, adoption of AI-CAD is expanding, with systems approved by the Korean Food and Drug Administration currently in clinical use. Recently, one AI-CAD system received conditional non-reimbursement designation, allowing hospitals to use it for up to 5 years while collecting clinical evidence to support future insurance coverage decisions.Discussion and Conclusion: AI-CAD has significant potential to enhance early breast cancer detection while maintaining acceptable false-positive rates, making it a valuable adjunct in screening programs. Beyond improved detection, AI-CAD may optimize workflow efficiency by triaging cases and prioritizing high-risk examinations. However, its integration into clinical practice necessitates standardized guidelines, regulatory oversight, and further validation through large-scale prospective studies. As AI technology continues to advance, ongoing investigation into its role in personalized breast cancer screening is essential.

Positional information plays a crucial role in embryonic pattern formation, yet its role in tooth development remains unexplored. In this study, we investigated the regional specification of lingual and buccal dental mesenchyme during tooth development. Tooth germs at the cap stage were dissected from mouse mandibles, and their lingual and buccal mesenchymal regions were separated for bulk RNA sequencing. Gene ontology analysis revealed that odontogenesis, pattern specification, and proliferation-related genes were enriched in the lingual mesenchyme, whereas stem cell development, mesenchymal differentiation, neural crest differentiation, and regeneration-related genes were predominant in the buccal mesenchyme. Reaggregation experiments using Wnt1^creERT/+; R26R^tdT/+ and WT mouse models demonstrated that lingual mesenchyme contributes to tooth formation, while buccal mesenchyme primarily supports surrounding tissues. Furthermore, only the lingual part of tooth germs exhibited odontogenic potential when cultured in vitro and transplanted under the kidney capsule. Bulk RNA transcriptomic analysis further validated the regional specification of the lingual and buccal mesenchyme. These findings provide novel insights into the molecular basis of positional information in tooth development and pattern formation.
Animals ; Mice ; Odontogenesis/genetics* ; Tooth Germ/cytology* ; Mesoderm/cytology* ; Cell Differentiation ; Mesenchymal Stem Cells ; Tooth/embryology*

Purpose: Unnecessary prostate biopsies for detecting prostate cancer (PCa) should be minimized. Therefore, this study developed a machine learning (ML) model to predict PCa in Korean men and evaluated its usability. Materials and Methods: We retrospectively analyzed clinical data from 928 patients who underwent prostate biopsies at Kangwon National University Hospital between May 2013 and May 2023. Of these, 377 (41.6%) were diagnosed with PCa, and 551 (59.4%) did not have cancer. For external validation, clinical data from 385 patients aged 48–89 years who underwent prostate biopsies from September 2005 to September 2023 at Wonju Severance Christian Hospital were also included. Twenty-two clinical features were used to develop an ML model to predict PCa. Features were selected based on their contributions to model performance, leading to the inclusion of 15 features. A meta-learner was constructed using logistic regression to predict the probability of PCa, and the classifier was trained and validated on randomly extracted training and test sets at an 8:2 ratio. Results: The prostate health index, prostate volume, age, nodule on digital rectal examination, and prostate-specific antigen were the top 5 features for predicting PCa. The area under the receiver operating characteristic curve (AUC) of the meta-learner logistic regression model was 0.89, and the accuracy, sensitivity, and specificity were 0.828, 0.711, and 0.909, respectively. Our model also showed excellent prediction performance for high-grade PCa, with a Gleason score of 7 or higher and an AUC of 0.903. Furthermore, we evaluated the performance of the model using external cohort clinical data and achieved an AUC of 0.863. Conclusions Our ML model excelled in predicting PCa, specifically clinically significant PCa. Although extensive cross-validation in other clinical cohorts is needed, this ML model is a promising option for future diagnostics.

Tattooing is an invasive procedure that involves the introduction of permanent pigments into the dermis, and is categorized as a medical procedure in Korea. Despite the medical, cosmetic, and aesthetic purposes of tattooing, legislative proposals to allow non-medical personnel to perform tattooing have consistently been rejected on public health grounds, prioritizing health and safety considerations. Professional organizations have maintained a consistent position, highlighting the risks of allowing non-medical individuals to perform tattooing. These risks include procedural complications, use of unsafe practices, and inadequate legal frameworks to ensure accountability.Addressing such issues requires careful consideration beyond economic or convenience factors, with an emphasis on public health policies. To address these concerns, the Korean Dermatological Research Foundation convened its 1st Policy Forum on October 6, 2024. The forum discussed the safety aspects, medical complications, and legal implications of tattooing performed by non-medical personnel. These findings highlight the significant risks and regulatory gaps associated with such practices, underscoring the need for a comprehensive review and stringent regulations to protect public health.

Tattooing is an invasive procedure that involves the introduction of permanent pigments into the dermis, and is categorized as a medical procedure in Korea. Despite the medical, cosmetic, and aesthetic purposes of tattooing, legislative proposals to allow non-medical personnel to perform tattooing have consistently been rejected on public health grounds, prioritizing health and safety considerations. Professional organizations have maintained a consistent position, highlighting the risks of allowing non-medical individuals to perform tattooing. These risks include procedural complications, use of unsafe practices, and inadequate legal frameworks to ensure accountability.Addressing such issues requires careful consideration beyond economic or convenience factors, with an emphasis on public health policies. To address these concerns, the Korean Dermatological Research Foundation convened its 1st Policy Forum on October 6, 2024. The forum discussed the safety aspects, medical complications, and legal implications of tattooing performed by non-medical personnel. These findings highlight the significant risks and regulatory gaps associated with such practices, underscoring the need for a comprehensive review and stringent regulations to protect public health.

Objectives: Presenteeism is known to be a much more economically damaging social cost than disease rest while going to work despite physical pain. Since COVID-19, social discussions on the sickness benefit have been taking place as a countermeasure against presenteeism, and in particular, farmers and fishermen do not have an institutional mechanism for livelihood support when a disease other than work occurs. This study attempted to examine the relationship between agricultural, fishing, and forestry workers and presenteeism using the 6th Korean Work Conditions Survey. Methods: From October 2020 to January 2021, data from the 6th working conditions survey conducted on 17 cities and provinces in Korea were used, and a total of 34,981 people were studied. Control variables were gender, age, self-health assessment, education level, night work, shift work, monthly income, occupation, working hours per week, and employment status. Results: As a result of the analysis, farmers and fishermen showed the characteristics of the self-employed and the elderly, and as a result of the regression analysis, when farmers and fishermen analyzed the relationship with presenteeism tendency compared to other industry workers, farmers and fishermen increased by 23% compared to other industry groups. Conclusion This study is significant in that it has representation by utilizing the 6th working conditions survey and objectively suggests the need for a sickness benefit for farmers and fishermen who may be overlooked in the sickness benefit.

Background: and Purpose: Dementia, particularly Alzheimer’s disease, is a significant global health concern, with early diagnosis and treatment development being critical goals. While numerous cohorts have advanced dementia research, there is a lack of comprehensive data on ethnic differences, particularly for the Korean population. The Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD) aims to establish a large-scale, hospital-based dementia cohort to address this gap, with a focus on understanding disease progression, developing early diagnostics, and supporting treatment advancements specific to the Korean population. Methods: K-ROAD comprises multiple prospective cohorts. Participants underwent clinical evaluations, neuroimaging, and biomarker analysis, with data collected on a range of clinical and genomic markers. Results: As of December 2023, K-ROAD has recruited over 5,800 participants, including individuals across the Alzheimer’s clinical syndrome, subcortical vascular cognitive impairment, and frontotemporal dementia spectra. Preliminary findings highlight significant ethnic differences in amyloid positivity, cognitive decline, and biomarker profiles, compared to Western cohorts. Conclusions The K-ROAD cohort offers a unique and critical resource for dementia research, providing insights into ethnic-specific disease characteristics and biomarker profiles. These findings will contribute to the development of personalized diagnostic and therapeutic approaches to dementia, enhancing global understanding of the disease.

Background: and Purpose: We aimed to develop the diagnostic matrix of the Seoul Cognitive Status Test (SCST) and compare its performance with traditional paper-and-pencil neuropsychological tests, including the Seoul Neuropsychological Screening Battery-II (SNSB-II) and the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD-K). Methods: We recruited 197 participants from the head-to-head SCST-SNSB cohort, and 204 participants from the head-to-head SCST-CERAD cohort. They underwent either SNSB-II or CERAD-K, in addition to SCST. The diagnostic matrix was developed by combining cognitive function, determined by neuropsychological tests, and activities of daily living (ADL), determined by Instrumental-ADL scales. Results: The diagnostic agreement between the SCST and the SNSB-II was 83.9% (weighted kappa=0.87). The agreement between the SCST and the CERAD-K was 84.3% (weighted kappa=0.88). In the SCST-SNSB cohort, all differences in SCST scores between the cognitively unimpaired (CU), mild cognitive impairment (MCI), and dementia diagnosed with the SNSB-II were significant in all cognitive domains (all p<0.01), except for the executive domain between CU and MCI (p=0.145). In the SCST-CERAD cohort, all differences in SCST scores between the 3 groups diagnosed with the CERAD-K were significant in all cognitive domains (all p<0.01), except for the language and visuospatial domains between MCI and dementia (p=0.169 and p=0.778, respectively). Conclusions Our findings suggest that the tablet-based SCST may be another option to traditional paper-and-pencil neuropsychological tests, especially in situations where time and space are relatively limited, and neuropsychological testing specialists are not available.