Use of Artificial Intelligence in Clinical Biochemistry
DOI:
https://doi.org/10.48047/r5kvmp56Keywords:
Clinical Biochemistry, Artificial Intelligence, Databases, Healthcare, Scientific understandingAbstract
Traditionally, clinical biochemistry has relied on manual interpretation of laboratory test results, a process that
can be time-consuming, susceptible to human error, and limited in scope. However, the integration of artificial
intelligence (AI) technologies, including machine learning (ML) and deep learning, has significantly transformed
this field. AI-driven predictive modeling offers medical professionals the ability to enhance treatment planning,
anticipate disease progression, and reduce adverse outcomes. These technological advancements ultimately
contribute to improved patient care and more efficient resource utilization. For this review, titled "Use of Artificial
Intelligence in Biochemistry," an extensive literature search was conducted across multiple databases such as
PubMed, Google Scholar, Scopus, and Web of Science. The search focused on articles published within the
past decade, utilizing MeSH terms like (artificial intelligence) AND (clinical) AND (biochemistry), as well as
additional keywords, including "machine learning," "deep learning," "biochemical analysis," and "clinical
applications." The key characteristics of the selected studies are summarized in the accompanying table.
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