Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

Background: The accuracy of Logical Observation Identifiers Names and Codes (LOINC) mappings is reportedly low, and the LOINC codes used for research purposes in Korea have not been validated for accuracy or usability. Our study aimed to evaluate the discrepancies and similarities in interoperability using existing LOINC mappings in actual patient care settings. Methods: We collected data on local test codes and their corresponding LOINC mappings from seven university hospitals. Our analysis focused on laboratory tests that are frequently requested, excluding clinical microbiology and molecular tests. Codes from nationwide proficiency tests served as intermediary benchmarks for comparison. A research team, comprising clinical pathologists and terminology experts, utilized the LOINC manual to reach a consensus on determining the most suitable LOINC codes. Results: A total of 235 LOINC codes were designated as optimal codes for 162 frequent tests.Among these, 51 test items, including 34 urine tests, required multiple optimal LOINC codes, primarily due to unnoted properties such as whether the test was quantitative or qualitative, or differences in measurement units. We analyzed 962 LOINC codes linked to 162 tests across seven institutions, discovering that 792 (82.3%) of these codes were consistent. Inconsistencies were most common in the analyte component (38 inconsistencies, 33.3%), followed by the method (33 inconsistencies, 28.9%), and properties (13 inconsistencies, 11.4%). Conclusion This study reveals a significant inconsistency rate of over 15% in LOINC mappings utilized for research purposes in university hospitals, underlining the necessity for expert verification to enhance interoperability in real patient care.

Background: This study established sex-specific reference intervals (RIs) for hematological parameters, including the monocyte distribution width (MDW), in Korean adults and assessed the need for separate RIs according to sex in older adults. Methods: Hematological parameters were measured using a DxH 900 hematology analyzer (Beckman Coulter, USA) on 328 peripheral blood samples from 124 men and 204 women aged 19–93 years. Results: The RIs for white blood cells, red blood cells (RBCs), Hb, Hct, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, monocyte percentage, and neutrophil, eosinophil, lympho­cyte, and monocyte counts differed significantly by sex (P<0.05). Mean corpuscular volume was significantly positively associated with age, whereas RBC and platelet counts were significantly negatively correlated with age in both men and women. Age-based analysis revealed significant shifts in the RBC parameters beginning at 60 years of age in men and 70 years of age in women. The MDW did not show significant differences according to sex or age. Conclusions This study supports the use of sex-specific RIs and highlights the importance of considering age when interpreting results regarding hematological parameters in clinical practice.

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.