The rapid integration of large language models into medical publishing offers considerable potential for improving drafting efficiency but simultaneously raises substantial concerns regarding research integrity, accountability, and the reliability of the scientific record. Recent incidents in which artificial intelligence (AI) systems were listed as coauthors have prompted urgent regulatory revisions. In this review, we identify a global consensus that strictly prohibits AI authorship, as algorithms lack both moral agency and legal accountability. Transparency has emerged as an essential requirement, and undisclosed AI use is increasingly regarded as a form of ethical misconduct. Key risks include “hallucinations” (notably citation fabrication), algorithmic bias, and potential violations of privacy regulations (e.g., the Health Insurance Portability and Accountability Act) when protected health information is processed through cloud-based platforms. The analysis indicates that rigid prohibitions are operationally unenforceable, supporting instead a “human stewardship” model in which AI functions as a drafting scaffold subjected to rigorous human verification. AI represents a lasting transformation in medical writing that necessitates a shift from simple prohibition to structured governance. To preserve epistemic validity, we propose a framework built on task segmentation, mandatory cross-referencing of claims, and data sovereignty. Ultimately, AI must remain a transparent assistive tool, with full responsibility for the manuscript residing exclusively with the human investigator.

Background: Accurate assessment of liver fibrosis is critical for the management of chronic liver disease. Noninvasive biomarkers are increasingly being investigated as alternatives to liver biopsy. Platelet count has emerged as a potential predictor of advanced fibrosis and may complement established indices such as the fibrosis-4 (FIB-4) score and the aspartate aminotransferase-to-platelet ratio index (APRI). Methods: This prospective analysis included 101 patients with histologically confirmed data obtained through liver biopsy or hepatic resection. Platelet count, APRI, FIB-4, Model for End-Stage Liver Disease score, Mac-2 binding protein glycosylation isomer (M2BPGi), and albumin-bilirubin score were measured and correlated with fibrosis stage using the METAVIR scoring system. Logistic regression analysis and receiver operating characteristic (ROC) curve analysis were performed to assess the predictive performance of each marker. Results: Platelet count demonstrated an inverse correlation with fibrosis severity and was identified as the most reliable predictor of advanced fibrosis (METAVIR ≥3), with an area under the ROC curve of 0.822. Using a cutoff value of 184,000, platelet count yielded a sensitivity of 69.2% and a specificity of 87.8% for the detection of significant fibrosis. Conclusions Platelet count is a simple, widely available, and robust predictor of liver fibrosis, outperforming APRI, FIB-4, and M2BPGi in multivariate analysis. Validation in larger, independent cohorts is warranted to confirm its clinical utility.

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.