Use of Artificial Intelligence in Clinical Biochemistry

Traditionally, clinical biochemistry has relied on manual interpretation of laboratory test results, a process thatcan be time-consuming, susceptible to human error, and limited in scope. However, the integration of artificialintelligence (AI) technologies, including machine learning (ML) and deep learning, has significantly transformedthis field. AI-driven predictive modeling offers medical professionals the ability to enhance treatment planning,anticipate disease progression, and reduce adverse outcomes. These technological advancements ultimatelycontribute to improved patient care and more efficient resource utilization. For this review, titled "Use of ArtificialIntelligence in Biochemistry," an extensive literature search was conducted across multiple databases such asPubMed, Google Scholar, Scopus, and Web of Science. The search focused on articles published within thepast decade, utilizing MeSH terms like (artificial intelligence) AND (clinical) AND (biochemistry), as well asadditional keywords, including "machine learning," "deep learning," "biochemical analysis," and "clinicalapplications." The key characteristics of the selected studies are summarized in the accompanying table.