Objectives: Large language models (LLMs) demonstrate remarkable potential in healthcare communication. However, whether they can process complex, high-volume medical information, such as stroke-related content, remains insufficiently validated. This study aimed to evaluate the natural language processing capabilities of LLMs in handling such content and to develop an evaluation instrument. Methods: A survey compared educational materials generated by two LLMs (ChatGPT 4.0 and Claude 3) with neurologist-authored content on stroke. The materials were based on two clinical scenarios representing distinct stroke etiologies: cardioembolism and large-artery atherosclerosis. They were evaluated in terms of accuracy, legality, ethics, comprehensiveness, and information delivery. Scores for comprehensiveness and information delivery were compared according to participants’ agreement with the use of LLMs in healthcare. Results: ChatGPT received the highest scores across all domains, except for legality in Scenario 2. In Scenario 1, the ranking for accuracy and summarization of clinical information was, from highest to lowest, ChatGPT, Claude, and the neurologist (η2 = 0.140, p < 0.001; η2 = 0.175, p < 0.001). The same hierarchy was observed in Scenario 2 for accuracy (η2 = 0.077, p < 0.001) and summarization (η2 = 0.194, p < 0.001). Participants who agreed with the use of LLMs in healthcare assigned higher scores for the comprehensiveness (Scenario 1, p = 0.005; Scenario 2, p = 0.007) and information delivery (Scenario 1, p = 0.003; Scenario 2, p = 0.026) of ChatGPT-generated materials than participants who did not agree. Conclusions LLMs demonstrated adequate capability to convey complex content, such as stroke-related information, in an accessible and understandable manner for non-experts.

Purpose: High-cost novel therapies for multiple myeloma (MM) require evaluation of efficacy and cost-effectiveness. Materials and Methods: This study developed a methodology to assess cost-effectiveness using nationwide data from 11,450 newly diagnosed MM patients. A novel algorithm was applied to identify lines of therapy (LoT). Results: The number of newly diagnosed MM patients increased significantly, from 873 in 2010 to 1,464 in 2019 (p < 0.001). Advancing LoT was associated with shorter time to next treatment (TTNT) and overall survival (OS) (p < 0.001), while all-cause medical costs increased with each LoT (p < 0.001). Bortezomib-melphalan-prednisolone was the most common frontline regimen for transplant-ineligible patients (29.2%), while bortezomib-thalidomide-dexamethasone was most used for transplant-eligible patients (11.3%). Daratumumab monotherapy demonstrated superior second TTNT (7.8 vs. 5.2 months) and OS (8.5 vs. 5.3 months) compared to standard care in heavily treated MM patients, with statistical significance maintained after cost adjustment. For subsequent therapies following daratumumab, a methodology was developed to estimate required medical costs using the incremental cost-effectiveness ratio (ICER): Expected cost ($)=ICER×(Expected life expectancy–0.567)+35,601. Conclusion This study provides a novel cost-effectiveness framework linking treatment efficacy and real-world costs, supporting predictions of societal costs for future MM therapies.

Background: Cisplatin-based chemotherapy is a mainstay treatment for head and neck squamous cell carcinoma (HNSC); however, resistance to cisplatin contributes substantially to poor clinical outcomes. Identifying biomarkers associated with cisplatin response may improve prognostic assessment and treatment selection. Methods: We retrospectively analyzed The Cancer Genome Atlas (TCGA)-HNSC dataset to evaluate the association between microtubule associated scaffold protein 1 (MTUS1) expression and clinical outcomes, with particular emphasis on patients who were cisplatin-treated. Survival analysis was performed using the Kaplan-Meier curves, and differential expression analysis was conducted separately by comparing patients in disease-specific survival (DSS)-living and DSS-deceased groups. MTUS1 messenger RNA and protein levels were examined in cisplatin-sensitive oral cancer cell lines and their paired cisplatin-resistant counterparts using quantitative reverse transcription polymerase chain reaction and western blotting. Functional relevance was assessed by small interfering RNA-mediated MTUS1 knockdown in primary oral squamous cell carcinoma organoids. Results: MTUS1 protein expression was significantly lower in HNSC tumors than in non-tumor tissues. In the overall TCGA-HNSC cohort, MTUS1 expression was not significantly associated with survival. However, in patients who were cisplatin-treated, higher MTUS1 expression was significantly associated with more favorable DSS. MTUS1 expression was consistently lower in cisplatin-resistant oral cancer cell lines than in their paired cisplatin-sensitive counterparts. Functional experiments further suggested that reduced MTUS1 expression is associated with decreased cisplatin sensitivity and a resistant phenotype. Conclusion MTUS1 expression may be associated with clinical outcomes in patients with cisplatin-treated HNSC and is related to cisplatin responsiveness. These findings suggest a role for MTUS1 as a candidate treatment-relevant biomarker and highlight the value of integrating public omics data with experimental validation.

The multi-decade household humidifier disinfectant poisoning tragedy (HHDT) in South Korea highlights the importance of investigating government failures. This study aims to identify and discuss key failures and shortcomings in the South Korean authorities’ approach to regulating humidifier disinfectants (HDs) and monitoring cases of chemical poisoning. We reviewed both the HD risk prevention measures that the South Korean Ministry of Environment (KME) should have implemented under the Toxic Chemicals Control Act (TCCA) (1991–2013).Polyhexamethylene guanidine phosphate (PHMG), a new chemical, was approved for use as a disinfectant under the TCCA. KME declared PHMG non-hazardous based solely on preproduction documentation provided by the industry. In addition, the Korea Disease Control and Prevention Agency (KDCPA) failed to detect the HHDT that had accumulated each year for more than a decade. KME’s neglect of its responsibilities, coupled with KDCPA’s lack of chemical poisoning surveillance systems, led to the accumulation of widespread HHDT.

The multi-decade household humidifier disinfectant poisoning tragedy (HHDT) in South Korea highlights the importance of investigating government failures. This study aims to identify and discuss key failures and shortcomings in the South Korean authorities’ approach to regulating humidifier disinfectants (HDs) and monitoring cases of chemical poisoning. We reviewed both the HD risk prevention measures that the South Korean Ministry of Environment (KME) should have implemented under the Toxic Chemicals Control Act (TCCA) (1991–2013).Polyhexamethylene guanidine phosphate (PHMG), a new chemical, was approved for use as a disinfectant under the TCCA. KME declared PHMG non-hazardous based solely on preproduction documentation provided by the industry. In addition, the Korea Disease Control and Prevention Agency (KDCPA) failed to detect the HHDT that had accumulated each year for more than a decade. KME’s neglect of its responsibilities, coupled with KDCPA’s lack of chemical poisoning surveillance systems, led to the accumulation of widespread HHDT.

The multi-decade household humidifier disinfectant poisoning tragedy (HHDT) in South Korea highlights the importance of investigating government failures. This study aims to identify and discuss key failures and shortcomings in the South Korean authorities’ approach to regulating humidifier disinfectants (HDs) and monitoring cases of chemical poisoning. We reviewed both the HD risk prevention measures that the South Korean Ministry of Environment (KME) should have implemented under the Toxic Chemicals Control Act (TCCA) (1991–2013).Polyhexamethylene guanidine phosphate (PHMG), a new chemical, was approved for use as a disinfectant under the TCCA. KME declared PHMG non-hazardous based solely on preproduction documentation provided by the industry. In addition, the Korea Disease Control and Prevention Agency (KDCPA) failed to detect the HHDT that had accumulated each year for more than a decade. KME’s neglect of its responsibilities, coupled with KDCPA’s lack of chemical poisoning surveillance systems, led to the accumulation of widespread HHDT.

The multi-decade household humidifier disinfectant poisoning tragedy (HHDT) in South Korea highlights the importance of investigating government failures. This study aims to identify and discuss key failures and shortcomings in the South Korean authorities’ approach to regulating humidifier disinfectants (HDs) and monitoring cases of chemical poisoning. We reviewed both the HD risk prevention measures that the South Korean Ministry of Environment (KME) should have implemented under the Toxic Chemicals Control Act (TCCA) (1991–2013).Polyhexamethylene guanidine phosphate (PHMG), a new chemical, was approved for use as a disinfectant under the TCCA. KME declared PHMG non-hazardous based solely on preproduction documentation provided by the industry. In addition, the Korea Disease Control and Prevention Agency (KDCPA) failed to detect the HHDT that had accumulated each year for more than a decade. KME’s neglect of its responsibilities, coupled with KDCPA’s lack of chemical poisoning surveillance systems, led to the accumulation of widespread HHDT.

Background: The persistent coronavirus disease 2019 (COVID-19) pandemic has had direct and indirect effects on mortality, making it essential to analyze excess mortality to fully understand the impact of the pandemic. In this study, we constructed a mathematical model using number of deaths from Statistics Korea and analyzed excess mortality between 2020 and 2022 according to age, sex, and dominant severe acute respiratory syndrome coronavirus 2 variant period. Methods: Number of all-cause deaths between 2010 and 2022 were obtained from the annual cause-of-death statistics provided by Statistics Korea. COVID-19 mortality data were acquired from the Korea Disease Control and Prevention Agency. A multivariate linear regression model with seasonal effect, stratified by sex and age, was used to estimate the number of deaths in the absence of COVID-19. The estimated excess mortality rate was calculated. Results: Excess mortality was not significant between January 2020 and October 2021.However, it started to increase monthly from November 2021 and reached its highest point during the omicron-dominant period. Specifically, in March and April 2022, during the omicron BA.1/BA.2-dominant period, the estimated median values for excess mortality were the highest at 17,634 and 11,379, respectively. Both COVID-19-related deaths and excess mortality increased with age. A notable increase in excess mortality was observed in individuals aged ≥ 65 years. In the context of excess mortality per 100,000 population based on the estimated median values in March 2022, the highest numbers were found among males and females aged ≥ 85 years at 1,048 and 910, respectively. Conclusion This study revealed that the prolonged COVID-19 pandemic coupled with its high transmissibility not only increased COVID-19-related deaths but also had a significant impact on overall mortality rates, especially in the elderly. Therefore, it is crucial to concentrate healthcare resources and services on the elderly and ensure continued access to healthcare services during pandemics. Establishing an excess mortality monitoring system in the early stages of a pandemic is necessary to understand the impact of infectious diseases on mortality and effectively evaluate pandemic response policies.

Background: The persistent coronavirus disease 2019 (COVID-19) pandemic has had direct and indirect effects on mortality, making it essential to analyze excess mortality to fully understand the impact of the pandemic. In this study, we constructed a mathematical model using number of deaths from Statistics Korea and analyzed excess mortality between 2020 and 2022 according to age, sex, and dominant severe acute respiratory syndrome coronavirus 2 variant period. Methods: Number of all-cause deaths between 2010 and 2022 were obtained from the annual cause-of-death statistics provided by Statistics Korea. COVID-19 mortality data were acquired from the Korea Disease Control and Prevention Agency. A multivariate linear regression model with seasonal effect, stratified by sex and age, was used to estimate the number of deaths in the absence of COVID-19. The estimated excess mortality rate was calculated. Results: Excess mortality was not significant between January 2020 and October 2021.However, it started to increase monthly from November 2021 and reached its highest point during the omicron-dominant period. Specifically, in March and April 2022, during the omicron BA.1/BA.2-dominant period, the estimated median values for excess mortality were the highest at 17,634 and 11,379, respectively. Both COVID-19-related deaths and excess mortality increased with age. A notable increase in excess mortality was observed in individuals aged ≥ 65 years. In the context of excess mortality per 100,000 population based on the estimated median values in March 2022, the highest numbers were found among males and females aged ≥ 85 years at 1,048 and 910, respectively. Conclusion This study revealed that the prolonged COVID-19 pandemic coupled with its high transmissibility not only increased COVID-19-related deaths but also had a significant impact on overall mortality rates, especially in the elderly. Therefore, it is crucial to concentrate healthcare resources and services on the elderly and ensure continued access to healthcare services during pandemics. Establishing an excess mortality monitoring system in the early stages of a pandemic is necessary to understand the impact of infectious diseases on mortality and effectively evaluate pandemic response policies.

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application.This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.