Artificial Intelligence-Driven Biomarker Discovery in Clinical Biochemistry: Opportunities and Challenges

Abstract

Background: Biomarkers are measurable biological characteristics that provide information about normal physiological processes, disease mechanisms, disease progression, treatment response, or prognosis. Clinical biochemistry generates large quantities of biomarker information through analysis of proteins, enzymes, metabolites, hormones, nucleic acids, and other molecular components. The rapid development of high-throughput technologies has resulted in increasingly complex and high-dimensional datasets that are difficult to analyze using conventional statistical approaches alone. Artificial intelligence (AI), including machine learning and deep learning, provides new approaches for identifying patterns, integrating multimodal datasets, selecting candidate biomarkers, and developing predictive biomarker signatures. Objective: To review the applications of artificial intelligence in biomarker discovery within clinical biochemistry, with emphasis on machine learning, deep learning, multi-omics integration, biomarker selection, diagnostic and prognostic applications, explainable artificial intelligence, and the major challenges affecting clinical translation. Methods: A narrative literature review was undertaken over a period of 6–8 months at Mahavir Institute of Medical Sciences. Relevant literature addressing artificial intelligence, machine learning, deep learning, biomarker discovery, clinical biochemistry, laboratory medicine, multi-omics, metabolomics, proteomics, genomics, diagnostic biomarkers, prognostic biomarkers, and precision medicine was reviewed. PubMed-indexed publications and major biomedical literature were considered, with particular emphasis on recent reviews, methodological studies, translational investigations, and clinically relevant applications. The literature was synthesized thematically according to major stages of AI-assisted biomarker discovery and clinical implementation. Results: The reviewed literature indicates that AI can facilitate biomarker discovery by identifying complex associations within high-dimensional molecular datasets and integrating information from genomics, transcriptomics, proteomics, metabolomics, electronic health records, imaging, and other clinical data sources. Machine learning approaches including random forests, support vector machines, regularized regression, clustering methods, and gradient-boosting algorithms have been used for feature selection, classification, patient stratification, and biomarker signature development. Deep learning approaches can further analyze highly complex and multimodal datasets, although they frequently require large datasets and may present challenges regarding interpretability. Multi-omics integration provides opportunities to identify biomarkers that reflect multiple biological layers rather than individual molecular measurements. Explainable AI may improve biological interpretation and facilitate mechanistic validation of computationally identified biomarkers. However, overfitting, data leakage, population bias, inadequate external validation, lack of standardized laboratory measurements, dataset heterogeneity, limited interpretability, regulatory uncertainty, and difficulties in translating computational biomarkers into clinically usable tests remain important barriers. Conclusion: Artificial intelligence has considerable potential to transform biomarker discovery in clinical biochemistry by enabling analysis of complex molecular data and integration of multiple biological and clinical information sources. However, computational identification alone does not establish clinical validity or utility. Future development should emphasize high-quality datasets, appropriate feature selection, independent external validation, analytical standardization, explainable models, prospective clinical evaluation, and collaboration between clinical biochemists, clinicians, data scientists, and laboratory scientists. AI-assisted biomarker discovery is likely to become an important component of precision medicine if methodological robustness and clinical applicability are addressed simultaneously. Keywords: artificial intelligence; biomarker discovery; clinical biochemistry; machine learning; deep learning; multi-omics; metabolomics; proteomics; precision medicine; laboratory medicine