Artificial Intelligence and Machine Learning in Biochemical and Molecular Diagnostics: A Transformative Review of Current Applications and Future Prospects
DOI:
https://doi.org/10.22399/ijcesen.2634Keywords:
Artificial Intelligence, Machine Learning , Biochemical Diagnostics, Molecular Diagnostics, Clinical Decision Support, Biomarker DetectionAbstract
Advancements in artificial intelligence (AI) and machine learning (ML) are rapidly transforming the landscape of biochemical and molecular diagnostics. These technologies have demonstrated exceptional capabilities in processing large-scale omics data, identifying subtle biomarker patterns, and enhancing diagnostic accuracy across a wide range of diseases. This review aims to provide a comprehensive overview of current AI/ML applications in biochemical and molecular diagnostics, highlighting their integration in laboratory test interpretation, metabolomic profiling, genomic variant annotation, and transcriptomic analysis. We examine the role of machine learning algorithms such as support vector machines, random forests, and deep neural networks in enabling predictive, high-throughput, and personalized diagnostics. Additionally, the review addresses key challenges including data standardization, model interpretability, and clinical validation. Emerging trends such as federated learning, real-time diagnostics, and AI-integrated multi-omics platforms are discussed as promising frontiers. By synthesizing current findings and projecting future directions, this review underscores the transformative potential of AI and ML in advancing precision diagnostics and personalized medicine.
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