The ICT (information and communication technology) paradigm shift, including the burgeoning use of mobile, SNS, and smart devices, has resulted in an explosion of data along with lifestyle changes. We have thus arrived at the age of big data. In the meantime, a number of difficulties have arisen in terms of cost or on the technical side with respect to the use of large quantities of data. However, big data has begun to receive attention with the advent of efficient big data technologies such as Hadoop. Big data is recognized as the 21st century digital resource and new driving force. The governments of the United States, Japan, and other countries around the world have already been actively promoting big data in their national policies. Korea is promotingbig data actively in the public and private sectors according to National Issues of the Creative Economy and the Government 3.0. In this respect, the Korean government published the "Big Data Industry Development Strategy" in December 2013 and began supporting the use of big data in earnest. This article introduces the status and policy of international and domestic utilization of big data in the public sector. In addition, it describes the challenges in terms of technology, human resources, and data for the vitalization of big data collection and analysis in Korea.
Data Collection ; Explosions ; Humans ; Japan ; Korea ; Life Style ; Private Sector ; Public Sector* ; United States

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

The integration of artificial intelligence (AI) technologies into medical research introduces significant ethical challenges that necessitate the strengthening of ethical frameworks. This review highlights the issues of privacy, bias, accountability, informed consent, and regulatory compliance as central concerns. AI systems, particularly in medical research, may compromise patient data privacy, perpetuate biases if they are trained on nondiverse datasets, and obscure accountability owing to their “black box” nature. Furthermore, the complexity of the role of AI may affect patients’ informed consent, as they may not fully grasp the extent of AI involvement in their care. Compliance with regulations such as the Health Insurance Portability and Accountability Act and General Data Protection Regulation is essential, as they address liability in cases of AI errors. This review advocates a balanced approach to AI autonomy in clinical decisions, the rigorous validation of AI systems, ongoing monitoring, and robust data governance. Engaging diverse stakeholders is crucial for aligning AI development with ethical norms and addressing practical clinical needs. Ultimately, the proactive management of AI’s ethical implications is vital to ensure that its integration into healthcare improves patient outcomes without compromising ethical integrity.

Cyberlindnera fabianii (previously known as Hansenula fabianii, Pichia fabianii, and Lindnera fabianii) is a yeast species that forms a biofilm, allowing it to resist azole drugs. In this study, we report a case of fungemia with C. fabianii that was successfully treated with anidulafungin. In this case, the organism was initially misidentified as Candida utilis (with a high probability of 93%, suggesting good identification) using the VITEK 2 yeast identification card (YST ID; bio-Merieux, USA). The species responsible for the patient's fungemia was correctly identified after sequencing the internally transcribed spacer region and the D1/D2 domain of the large subunit (26S) rDNA gene. The CLSI M27-A3 broth microdilution method was used to determine the in vitro antifungal activity of anidulafungin and fluconazole against C. fabianii. The MICs of anidulafungin and fluconazole were found to be 0.03 microg/mL and 2 microg/mL, respectively. The patient recovered after 14 days of anidulafungin treatment.
Biofilms ; Candida ; Danazol ; DNA, Ribosomal ; Fluconazole ; Fungemia* ; Humans ; Pichia ; Yeasts

BACKGROUND: The detection of total anti-hepatitis A virus (anti-HAV) immunoglobulin (Ig) and IgM is important for diagnosing acute hepatitis A. Our laboratory introduced new commercial automated chemiluminescence immunoassays (CLIAs) for use in addition to pre-existing automated CLIA. We evaluated the rate of agreement in the detection of total anti-HAV Ig and IgM in serum samples between two automated CLIAs. METHODS: We analyzed 181 samples those were submitted for testing at Kangbuk Samsung Medical Center. We analyzed the rate of agreement between the ADVIA Centaur XP (Siemens, Germany) and the MODULAR ANALYTICS E170 (Roche, Switzerland) analyzers. We performed reverse transcription (RT)-PCR when there was a discrepancy between the results from the two analyzers. RESULTS: The agreement rates between the ADVIA Centaur XP and the MODULAR ANALYTICS E170 for total anti-HAV Ig and IgM were 97.2% and 98.9%, respectively. Discrepant results were obtained in seven cases; all were found to be HAV-negative based on RT-PCR analysis. CONCLUSIONS: The total anti-HAV Ig and IgM results obtained using the two automated analyzers were comparable. However, in cases of equivocal results tested by the ADVIA Centaur XP for anti-HAV IgM, retesting and follow-up testing of samples are recommended.
Hepatitis A ; Hepatitis A Antibodies ; Hepatitis A virus ; Immunoassay ; Immunoglobulin M ; Immunoglobulins ; Luminescence ; Reverse Transcription ; Viruses

Objective: We aimed to assess the level of public awareness regarding dyslipidemia and its management among the Korean population. Methods: We conducted a web- or mobile-based survey study targeting the general population, using various recruitment methods, between July 25, 2022 and August 26, 2022.The questionnaire consisted of 12 questions designed to collect demographic information and evaluate participants’ awareness and knowledge about dyslipidemia. Results: In total, 2,882 participants who completed the survey were included in the analysis.Among the participants, a substantial majority (89.1%) were familiar with the concepts of “good cholesterol” and “bad cholesterol,” while a comparatively lower percentage (just 46.7%) were acquainted with the term “dyslipidemia.” Noticeable variations in understanding were observed when examining specific aspects of dyslipidemia management, including diet, exercise, and pharmacotherapy. Conclusion The results of this survey underscore the significance of enhancing public awareness about dyslipidemia within the context of health literacy, demonstrating the necessity for a more comprehensive approach that includes education and policymaking to effectively manage dyslipidemia.

Objective: This study aimed to investigate the prevalence and status of dyslipidemia management among South Korean adults, as performed by the Korean Society of Lipid and Atherosclerosis under the name Dyslipidemia Fact Sheet 2022. Methods: We analyzed the lipid profiles, age-standardized and crude prevalence, management status of hypercholesterolemia and dyslipidemia, and health behaviors among Korean adults aged ≥20 years, using the Korea National Health and Nutrition Examination Survey data between 2007 and 2020. Results: In South Korea, the crude prevalence of hypercholesterolemia (total cholesterol ≥240 mg/dL or use of a lipid-lowering drug) in 2020 was 24%, and the age-standardized prevalence of hypercholesterolemia more than doubled from 2007 to 2020. The crude treatment rate was 55.2%, and the control rate was 47.7%. The crude prevalence of dyslipidemia (more than one out of three conditions [low-density lipoprotein-cholesterol ≥160 or the use of a lipid-lowering drug, triglycerides ≥200, or high-density lipoprotein-cholesterol (men and women) <40 mg/ dL]) was 40.2% between 2016 and 2020. However, it increased to 48.2% when the definition of hypo-high-density lipoprotein-cholesterolemia in women changed from <40 to <50 mg/dL. Conclusion Although the prevalence of hypercholesterolemia and dyslipidemia has steadily increased in South Korea, the treatment rate remains low. Therefore, continuous efforts are needed to manage dyslipidemia through cooperation between the national healthcare system, patients, and healthcare providers.