BACKGROUND AND OBJECTIVES: To prevent noise induced hearing losses caused by listening to music with personal listening devices for young adults, this study was aimed to measure output levels of an MP3 and to identify preferred listening levels (PLLs) depending on earphone types, music genres, and listening durations. SUBJECTS AND METHODS: Twenty-two normal hearing young adults (mean=18.82, standard deviation=0.57) participated. Each participant was asked to select his or her most PLLs when listened to Korean ballade or dance music with an earbud or an over-the-ear earphone for 30 or 60 minutes. One side of earphone was connected to the participant’s better ear and the other side was connected to a sound level meter via a 2 or 6 cc-couplers. Depending on earphone types, music genres, and listening durations, loudness A-weighted equivalent (LAeq) and loudness maximum time-weighted with A-frequency sound levels in dBA were measured. RESULTS: Neither main nor interaction effects of the PLLs among the three factors were significant. Overall output levels of earbuds were about 10-12 dBA greater than those of over-the-ear earphones. The PLLs were 1.73 dBA greater for earbuds than over-the-ear earphones. The average PLL for ballad was higher than for dance music. The PLLs at LAeq for both music genres were the greatest at 0.5 kHz followed by 1, 0.25, 2, 4, 0.125, 8 kHz in the order. CONCLUSIONS: The PLLs were not different significantly when listening to Korean ballad or dance music as functions of earphone types, music genres, and listening durations. However, over-the-ear earphones seemed to be more suitable to prevent noise induce hearing loss when listening to music, showing lower PLLs, possibly due to isolation from the background noise by covering ears.
Dancing ; Ear ; Hearing ; Hearing Loss ; Humans ; MP3-Player ; Music ; Noise ; Young Adult

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: Procalcitonin (PCT) is a crucial biomarker for diagnosing sepsis and managing antibiotic therapy. This study evaluated the analytical performance and comparability of the Access PCT and Elecsys BRAHMS PCT assays. Methods: The precision, detection capability, linearity, and reference range of both assays were assessed. A comparative analysis included 182 patient samples categorized into four risk groups to compare the results between Access PCT and Elecsys BRAHMS PCT assays. Results: The Access PCT assay demonstrated precision within the manufacturer’s threshold, and its detection capabilities were verified. This assay exhibited excellent linearity and appropriate reference intervals. Comparative analysis indicated that the Access PCT assay reported higher overall PCT levels than the Elecsys BRAHMS assay, with high agreement between the assays (κ=0.941). However, the biases varied across different PCT concentration intervals. Conclusions Both the Access PCT and Elecsys BRAHMS PCT assays performed robustly with notable concordance but varying biases at different concentration intervals. The observed biases require careful consideration in clinical decision-making, especially when adopting novel assay systems. Standardizing the calibration across different platforms is recommended to improve assay comparability.

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: Procalcitonin (PCT) is a crucial biomarker for diagnosing sepsis and managing antibiotic therapy. This study evaluated the analytical performance and comparability of the Access PCT and Elecsys BRAHMS PCT assays. Methods: The precision, detection capability, linearity, and reference range of both assays were assessed. A comparative analysis included 182 patient samples categorized into four risk groups to compare the results between Access PCT and Elecsys BRAHMS PCT assays. Results: The Access PCT assay demonstrated precision within the manufacturer’s threshold, and its detection capabilities were verified. This assay exhibited excellent linearity and appropriate reference intervals. Comparative analysis indicated that the Access PCT assay reported higher overall PCT levels than the Elecsys BRAHMS assay, with high agreement between the assays (κ=0.941). However, the biases varied across different PCT concentration intervals. Conclusions Both the Access PCT and Elecsys BRAHMS PCT assays performed robustly with notable concordance but varying biases at different concentration intervals. The observed biases require careful consideration in clinical decision-making, especially when adopting novel assay systems. Standardizing the calibration across different platforms is recommended to improve assay comparability.

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: Procalcitonin (PCT) is a crucial biomarker for diagnosing sepsis and managing antibiotic therapy. This study evaluated the analytical performance and comparability of the Access PCT and Elecsys BRAHMS PCT assays. Methods: The precision, detection capability, linearity, and reference range of both assays were assessed. A comparative analysis included 182 patient samples categorized into four risk groups to compare the results between Access PCT and Elecsys BRAHMS PCT assays. Results: The Access PCT assay demonstrated precision within the manufacturer’s threshold, and its detection capabilities were verified. This assay exhibited excellent linearity and appropriate reference intervals. Comparative analysis indicated that the Access PCT assay reported higher overall PCT levels than the Elecsys BRAHMS assay, with high agreement between the assays (κ=0.941). However, the biases varied across different PCT concentration intervals. Conclusions Both the Access PCT and Elecsys BRAHMS PCT assays performed robustly with notable concordance but varying biases at different concentration intervals. The observed biases require careful consideration in clinical decision-making, especially when adopting novel assay systems. Standardizing the calibration across different platforms is recommended to improve assay comparability.

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: Procalcitonin (PCT) is a crucial biomarker for diagnosing sepsis and managing antibiotic therapy. This study evaluated the analytical performance and comparability of the Access PCT and Elecsys BRAHMS PCT assays. Methods: The precision, detection capability, linearity, and reference range of both assays were assessed. A comparative analysis included 182 patient samples categorized into four risk groups to compare the results between Access PCT and Elecsys BRAHMS PCT assays. Results: The Access PCT assay demonstrated precision within the manufacturer’s threshold, and its detection capabilities were verified. This assay exhibited excellent linearity and appropriate reference intervals. Comparative analysis indicated that the Access PCT assay reported higher overall PCT levels than the Elecsys BRAHMS assay, with high agreement between the assays (κ=0.941). However, the biases varied across different PCT concentration intervals. Conclusions Both the Access PCT and Elecsys BRAHMS PCT assays performed robustly with notable concordance but varying biases at different concentration intervals. The observed biases require careful consideration in clinical decision-making, especially when adopting novel assay systems. Standardizing the calibration across different platforms is recommended to improve assay comparability.

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.