Purpose: Medical artificial intelligence (AI) is rapidly being integrated into clinical practice and healthcare systems, raising concerns regarding safety, accountability, and governance. Despite its increasing importance, empirical comparative studies examining differences in perceptions of medical AI among key expert groups remain limited. This study aimed to compare and analyze perceptions of medical AI among medical and non-medical professionals and to systematically identify commonalities and differences across policy- and governance-relevant domains. Methods: Focus group interviews using open-ended questions were conducted with 30 experts (15 medical and 15 non-medical professionals) who had direct experience with medical AI. Data were analyzed using inductive thematic analysis combined with qualitative comparative analysis. Analytical rigor was strengthened through independent coding and consensus-based discussions. Results: Both groups recognized the potential of medical AI to bring meaningful changes to healthcare systems. However, medical professionals primarily evaluated medical AI in terms of clinical applicability, patient safety, explainability, and accountability. In contrast, non-medical professionals emphasized technological maturity, scalability, data infrastructure, standardization, and system-integration potential. Group-specific patterns also emerged regarding perceived limitations, autonomy, educational priorities, and classification frameworks, particularly in relation to clinical risk management versus system-level design and governance considerations. Conclusion: Differences in perceptions of medical AI are systematically associated with distinct interpretive frames shaped by professional roles and responsibility structures. Effective implementation and policy design for medical AI therefore require an integrated approach that accounts for these structural differences. This study provides empirical evidence and a conceptual foundation for future quantitative and mixed-methods research on medical AI governance.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Purpose: To investigate whether using a continuous glucose monitoring (CGM) for the second time (2nd_CGM) would be effective after using it for the first time (1st_CGM), depending on age. Materials and Methods: This study included patients aged ≥40 years who were diagnosed with type 2 diabetes and had used a CGM at least twice between 2017 and 2021. Participants were divided into two groups based on their age: those aged <60 years and those aged ≥60 years. We assessed the glycemic control status of the 1st_CGM and 2nd_CGM, along with the glycemic variability. Results: Overall, 15 patients were included in the study. The mean glucose level in users aged <60 years significantly decreased (p<0.001) owing to the CGM use, while it did not increase in those aged ≥60 years. In users aged ≥60 years, the 1st_CGM group showed a significant decrease in blood glucose levels over time (p<0.05), whereas the 2nd_CGM group only showed a non-significant decreasing trend. The time in range tended to increase in those aged <60 years but decreased in those aged ≥60 years. In those aged <60 years, the mean amplitude of glycemic excursions (p<0.001), standard deviation (p<0.05), and coefficient of variation (p<0.001) significantly decreased. In those aged ≥60 years, these parameters exhibited a non-significant decreasing trend. Conclusion Glycemic effect and variability improved as expected with 1st_CGM use. However, 2nd_CGM did not significantly improve glycemic effect or variability in users aged ≥60 years, contrary to expectations. To address this issue, further investigation is needed to understand why, compared to 1st_CGM, 2nd_CGM fails to achieve better glycemic control in individuals aged ≥60 years.

Background/Aims: Statins are common lipid-lowering agents used in dyslipidemia. However, they increase serum creatinine phosphokinase (CPK) levels. Currently, there are no studies on the effect of thyroid-stimulating hormone (TSH) levels on CPK levels after statin administration. Therefore, this study aimed to investigate CPK level alterations after statin administration according to TSH quartiles in participants with euthyroidism. Methods: This retrospective analysis included 25,047 patients with euthyroidism. CPK levels were measured before and 6 months after statin administration. Normal TSH levels were divided into four quartiles, and the CPK levels and proportions of patients with normal CPK levels after statin administration for each TSH quartile were evaluated. Results: The baseline CPK level was significantly higher in the lowest TSH quartile (Q1) compared to the other quartiles but decreased after statin administration. Thus, the difference between the CPK levels and the other quartile groups was not significant. The proportion of patients with normal CPK levels was also significantly lowest in Q1 before statin administration; however, no significant difference was noted in the ratio among each group after statin administration. These findings were consistent with the findings of the analysis according to statin intensity. Conclusions In patients in the lowest TSH quartile of the normal TSH range, the CPK level decreased, and the proportion of normal CPK levels increased significantly after statin administration. However, similar changes were not observed in other TSH quartiles. Therefore, further studies are required to mechanistically confirm these conclusions.

Advertising in the medical and legal fields, which are among Korea's leading professions, has increasingly utilized major advertising platforms such as LawTalk and UNNI—two of the most prominent and contentious platforms in their respective fields. While it is generally unproblematic for professionals like lawyers and doctors to promote public interest through advertising on these commercial platforms, the creation of a profit-driven structure has the potential to undermine their professional ecosystems. This article explores the issues associated with advertising in the medical field through large commercial platforms, drawing on notable examples from the legal and medical fields in Korea. Specifically, we analyze two of the most popular yet controversial platforms in these sectors, LawTalk and UNNI. In Korea, the format and method of advertising are legal as long as they do not involve referring or soliciting clients, thereby making platform advertising lawful when used solely for that purpose. Nevertheless, it is crucial to prevent medical advertising platforms from establishing market monopolies by skirting various profit regulations and laws. In response to these concerns, the Korean Bar Association has prohibited all advertisements by platform companies. The medical community should closely examine the rationale and process behind this decision. Given the significant social influence of large corporate platforms and the unique social responsibilities of the medical and legal professions, future platform advertising should be subject to distinct legal and institutional regulations that differ from those applied to general services.

Remote collaborative care is a program that improves medical services by linking local and remote physicians with residents in areas where access to medical facilities is limited, utilizing information and communication technology. As a result, patients can obtain medical advice and counseling at local hospitals without needing to travel to distant facilities. This care model involves communication between doctors, facilitating the accurate transfer of medical information and reducing the risk of misunderstandings. For instance, managing conditions such as blood pressure or blood glucose is more straightforward because a local hospital can assess the patient's status while a remote hospital simultaneously provides high-quality, specialized medical services. With the rise in poorly controlled hypertension or diabetes, the need for remote collaborative care has also increased. This care model enables local hospitals to maintain continuous patient care with the support of remote facilities. This is particularly true following acute cardiovascular treatment, where local hospitals, assisted by remote institutions, can safely offer high-quality services such as rehabilitation and follow-up care. Although remote hospitals have many advantages with the increasing number of patients, many difficulties remain in commercializing unsystematized remote collaborative care. Specifically, low reimbursements for medical services must be addressed, proper equipment is needed, more time and effort must be invested, and the liability issue must also be dealt with. Nevertheless, remote collaborative care using information and communication technology will be necessary in the future. Medical staff need to objectively examine the advantages and disadvantages of remote collaborative care from various perspectives and find ways to revitalize it.

The advent of medical big data has increased the scope of the clinical use of such data; however, these data have raised serious concerns regarding personal privacy protection, which hinders their usage. For instance, as the pseudonymization or anonymization of data increases, the quality of its clinical use decreases. Thus, a balanced approach is required to maximize clinical data use while protecting personal information as much as possible. However, Korea’s existing laws mandate several kinds of consent; soliciting some of these types of consent can be cumbersome. Moreover, while the collection of medical data by hospitals requires considerable time and money, its ownership is difficult to ascertain. To bridge the enormous gap between the protection of personal information and the use of clinical data, the European Union and countries such as Finland have already proposed various modes of guaranteeing the free movement of personal information that simultaneously strengthen people’s personal rights. Similarly, Korea has initiated the MyData Service, although it faces several limitations. Therefore, this study reviews Korea’s current healthcare big data system, the laws governing data sharing and usage, and compares them with similar laws enacted by the European Union and Finland. It then provides future direction for Korea’s personal information protection legislation. Ultimately, governments must expand and elaborate upon the scope and content of personal information protection laws to enable the development of healthcare and other industries without sacrificing either personal information protection or clinical use of medical data.