Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objectives: This research aimed to develop a retrieval-augmented generation (RAG) based large language model (LLM) system that offers personalized and reliable responses to a wide range of concerns raised by Korean adolescents. Our work focuses on building a culturally reflective dataset and on designing and validating the system’s effectiveness by comparing the answer quality of RAG-based models with non-RAG models. Methods: Data were collected from the NAVER Knowledge iN platform, concentrating on posts that featured adolescents’ questions and corresponding expert responses during the period 2014–2024. The dataset comprises 3,874 cases, categorized by key negative emotions and the primary sources of worry. The data were processed to remove irrelevant or redundant content and then classified into general and detailed causes. The RAG-based model employed FAISS for similarity-based retrieval of the top three reference cases and used GPT-4o mini for response generation. The responses generated with and without RAG were evaluated using several metrics. Results: RAG-based responses outperformed non-RAG responses across all evaluation metrics. Key findings indicate that RAG-based responses delivered more specific, empathetic, and actionable guidance, particularly when addressing complex emotional and situational concerns. The analysis revealed that family relationships, peer interactions, and academic stress are significant factors affecting adolescents’ worries, with depression and stress frequently co-occurring. Conclusions This study demonstrates the potential of RAG-based LLMs to address the diverse and culture-specific worries of Korean adolescents. By integrating external knowledge and offering personalized support, the proposed system provides a scalable approach to enhancing mental health interventions for adolescents. Future research should concentrate on expanding the dataset and improving multiturn conversational capabilities to deliver even more comprehensive support.

Objectives: This research aimed to develop a retrieval-augmented generation (RAG) based large language model (LLM) system that offers personalized and reliable responses to a wide range of concerns raised by Korean adolescents. Our work focuses on building a culturally reflective dataset and on designing and validating the system’s effectiveness by comparing the answer quality of RAG-based models with non-RAG models. Methods: Data were collected from the NAVER Knowledge iN platform, concentrating on posts that featured adolescents’ questions and corresponding expert responses during the period 2014–2024. The dataset comprises 3,874 cases, categorized by key negative emotions and the primary sources of worry. The data were processed to remove irrelevant or redundant content and then classified into general and detailed causes. The RAG-based model employed FAISS for similarity-based retrieval of the top three reference cases and used GPT-4o mini for response generation. The responses generated with and without RAG were evaluated using several metrics. Results: RAG-based responses outperformed non-RAG responses across all evaluation metrics. Key findings indicate that RAG-based responses delivered more specific, empathetic, and actionable guidance, particularly when addressing complex emotional and situational concerns. The analysis revealed that family relationships, peer interactions, and academic stress are significant factors affecting adolescents’ worries, with depression and stress frequently co-occurring. Conclusions This study demonstrates the potential of RAG-based LLMs to address the diverse and culture-specific worries of Korean adolescents. By integrating external knowledge and offering personalized support, the proposed system provides a scalable approach to enhancing mental health interventions for adolescents. Future research should concentrate on expanding the dataset and improving multiturn conversational capabilities to deliver even more comprehensive support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objectives: This research aimed to develop a retrieval-augmented generation (RAG) based large language model (LLM) system that offers personalized and reliable responses to a wide range of concerns raised by Korean adolescents. Our work focuses on building a culturally reflective dataset and on designing and validating the system’s effectiveness by comparing the answer quality of RAG-based models with non-RAG models. Methods: Data were collected from the NAVER Knowledge iN platform, concentrating on posts that featured adolescents’ questions and corresponding expert responses during the period 2014–2024. The dataset comprises 3,874 cases, categorized by key negative emotions and the primary sources of worry. The data were processed to remove irrelevant or redundant content and then classified into general and detailed causes. The RAG-based model employed FAISS for similarity-based retrieval of the top three reference cases and used GPT-4o mini for response generation. The responses generated with and without RAG were evaluated using several metrics. Results: RAG-based responses outperformed non-RAG responses across all evaluation metrics. Key findings indicate that RAG-based responses delivered more specific, empathetic, and actionable guidance, particularly when addressing complex emotional and situational concerns. The analysis revealed that family relationships, peer interactions, and academic stress are significant factors affecting adolescents’ worries, with depression and stress frequently co-occurring. Conclusions This study demonstrates the potential of RAG-based LLMs to address the diverse and culture-specific worries of Korean adolescents. By integrating external knowledge and offering personalized support, the proposed system provides a scalable approach to enhancing mental health interventions for adolescents. Future research should concentrate on expanding the dataset and improving multiturn conversational capabilities to deliver even more comprehensive support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Objective: This study aimed to identify the factors contributing to post-traumatic growth (PTG) among nurses who experienced patient death during the coronavirus disease-2019 (COVID-19) pandemic and to evaluate the necessity of grief support is required. Methods: An online survey was conducted to assess the experiences of nurses at Ulsan University Hospital who lost patients during the past year of the pandemic. In total, 211 nurses were recruited. We obtained information on the participants’ demographic and clinical characteristics. For symptoms rating, we used the following scales: the Post-traumatic Growth Inventory (PTGI), Stress and Anxiety to Viral Epidemic-9 (SAVE-9), Patient Health Questionnaire (PHQ-9), Pandemic Grief Scale (PGS), and Utrecht Grief Rumination Scale (UGRS), and Grief Support in Healthcare Scale (GSHCS). Pearson’s correlation coefficients, linear regression, and mediation analysis were employed. Results: PTGI scores were significantly correlated with the SAVE-9 (r=0.31, p<0.01), PHQ-9 (r=0.31, p<0.01), PGS (r=0.28, p<0.01), UGRS (r=0.45, p<0.01), and GSHCS scores (r=0.46, p<0.01). The linear regression analysis revealed the factors significantly associated with PTGI scores: SAVE-9 (β=0.16, p=0.014), UGRS (β=0.29, p<0.001), and GSHCS (β=0.34, p<0.001). The mediation analysis revealed that nurses’ stress and anxiety about COVID-19 and grief rumination had a direct impact on PTG, with grief support serving as a significant mediator. Conclusion PTG was promoted by increases in the medical staff’s anxiety and stress related to COVID-19, grief rumination, and grief support. For the medical staff’s experience of bereavement to result in meaningful personal and professional growth, family members, colleagues, and other associates should provide thoughtful support.

Generative artificial intelligence (GenAI), including large language models such as GPT-4 and image-generation tools like DALL-E, is rapidly transforming the landscape of medical education. These technologies present promising opportunities for advancing personalized learning, clinical simulation, assessment, curriculum development, and academic writing. Medical schools have begun incorporating GenAI tools to support students’ self-directed study, design virtual patient encounters, automate formative feedback, and streamline content creation. Preliminary evidence suggests improvements in engagement, efficiency, and scalability. However, GenAI integration also introduces substantial challenges. Key concerns include hallucinated or inaccurate content, bias and inequity in artificial intelligence (AI)-generated materials, ethical issues related to plagiarism and authorship, risks to academic integrity, and the potential erosion of empathy and humanistic values in training. Furthermore, most institutions currently lack formal policies, structured training, and clear guidelines for responsible GenAI use. To realize the full potential of GenAI in medical education, educators must adopt a balanced approach that prioritizes accuracy, equity, transparency, and human oversight. Faculty development, AI literacy among learners, ethical frameworks, and investment in infrastructure are essential for sustainable adoption. As the role of AI in medicine expands, medical education must evolve in parallel to prepare future physicians who are not only skilled users of advanced technologies but also compassionate, reflective practitioners.

The use of artificial intelligence (AI) in thyroid ultrasound is bringing important changes to endocrine imaging, helping improve diagnostic accuracy and make the assessment of thyroid nodules more consistent. This review examines the current applications, technological approaches, clinical performance, adversities, and future directions of AI-based systems in thyroid ultrasound. Recent studies suggest that AI technologies hold significant potential in thyroid ultrasound, particularly in automated nodule detection, classification, and risk stratification. Deep learning models, particularly convolutional neural networks, achieve diagnostic accuracies exceeding 90% in distinguishing benign from malignant nodules, often matching or surpassing human radiologist performance. Current applications include Thyroid Imaging Reporting and Data System-based classification systems, lymph node metastasis prediction, and real-time diagnostic assistance. However, challenges including reproducibility concerns, clinical workflow integration, and regulatory considerations remain significant barriers to widespread adoption. While AI shows remarkable promise in thyroid ultrasound applications, challenges including validation requirements, standardization needs, and clinical integration barriers must be addressed for widespread adoption. Future developments should focus on multimodal integration, explainable AI systems, and prospective clinical trials to fully utilize the potential of AI in transforming thyroid diagnostics.