AI-Assisted Interpretation of Complex Biochemical Panels in Critical Care Units - Hasson, Fatima Hussein Talib; Shaker, Shaker Khalif Youssef; Saad, Marwa Hussein Shaker; Hasan, Yasameen Makki Abdul (American Journal of Biomedicine and Pharmacy, 2025)
Compiling patient biochemical tests into panels automates laboratory analysis especially in critical-care scenarios. Biochemical-panel analyses provide information on disease nature and severity. Consistent interpretation of clinical values supports accurate diagnoses, effective patient care, and data-driven decision-making at a broader level. Common tests include blood chemistry panels assessing the bloodstream according to clinical needs. Biochemical assays focus on molecules, enzymes, or other analytes of clinical interest that react with reagents to generate measurable signals. The resultant signal correlates with relative blue dots separation, enabling sample identification. Amperometric detection uses samples containing redox-active species stimulating current flow upon reaction with an initial potential sent through electrodes. Consequently, DNA separation-migration occurs regularly, producing mirror-image guanine ratios and valuable band-design information. Artificial intelligence contributes to well-informed and timely decisions based on large, diverse data. In emergency departments, where routine triage cannot keep pace with rapidly accumulating data, considerable attention has been devoted to developing computational systems facilitating diagnostic decision-making. Automating complex biochemical-panel interpretation constitutes a significant step toward open-source, self-sustained clinical decision support tools. AI methodologies encompass machine learning, natural-language processing (NLP), and data mining, all applicable to complex biochemical-panel analysis.