Next-Generation Biomarkers in Clinical Chemistry: Integrating Metabolomics and AI for Early Disease Detection
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This study explores the integration of metabolomics and artificial intelligence (AI) to develop next-generation biomarkers for early disease detection, addressing limitations of current diagnostic methods such as high costs, poor scalability, and interpretive complexity. Despite advances in omics technologies, the translation of metabolomics into clinical practice remains underdeveloped due to data heterogeneity and computational challenges. By employing machine learning algorithms on large-scale metabolomics datasets, the research demonstrates significant improvements in diagnostic accuracy across diseases like cancer, cardiovascular conditions, and neurodegenerative disorders. The Smart Diagnostics platform a microfluidic biosensor system integrated with AI enables real-time, low-cost, and minimally invasive testing with superior sensitivity and specificity compared to traditional methods. Results indicate that AI-augmented metabolomics can outperform conventional biomarkers and facilitate personalized, point-of-care medicine. This integrated framework presents a scalable solution to enhance early disease prognosis and streamline clinical workflows, especially in resource-limited settings.
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