Background: TP53 mutations are associated with poor prognosis in myelodysplastic neoplasm (MDS) and AML. The updated 5th WHO classification and International Consensus Classification (ICC) categorize TP53-mutated MDS and AML as unique entities. We conducted a multicenter study in Korea to investigate the characteristics of TP53-mutated MDS and AML, focusing on diagnostic aspects based on updated classifications. Methods: This study included patients aged ≥ 18 yrs who were diagnosed as having MDS(N = 1,244) or AML (N = 2,115) at six institutions. The results of bone marrow examination, cytogenetic studies, and targeted next-generation sequencing, including TP53, were collected and analyzed. Results: TP53 mutations were detected in 9.3% and 9.2% of patients with MDS and AML, respectively. Missense mutation was the most common, with hotspot codons R248/ R273/G245/Y220/R175/C238 accounting for 25.4% of TP53 mutations. Ten percent of patients had multiple TP53 mutations, and 78.4% had a complex karyotype. The median variant allele frequency (VAF) of TP53 mutations was 41.5%, with a notable difference according to the presence of a complex karyotype. According to the 5th WHO classification and ICC, the multi-hit TP53 mutation criteria were met in 58.6% and 75% of MDS patients, respectively, and the primary determinants were a TP53 VAF > 50% for the 5th WHO classification and the presence of a complex karyotype for the ICC. Conclusions Collectively, we elucidated the molecular genetic characteristics of patients with TP53-mutated MDS and AML, highlighting key factors in applying TP53 mutation-related criteria in updated classifications, which will aid in establishing diagnostic strategies.

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.

Background: 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 [HDL-C] [men and women] <40 mg/dL)—was 40.2% between 2016 and 2020. However, it increased to 48.2% when the definition of hypo-HDL-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.

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.