Artificial Intelligence Driven Multi-Omics Approaches for Precision Medicine: From Molecular Discovery to Clinical Translation

Abstract

Artificial intelligence-driven multi-omics approaches are increasingly important for precision medicine, yet their clinical value remains constrained by heterogeneous molecular data, limited interoperability, insufficient explainability, and uneven organizational readiness for implementation. This study aimed to quantitatively evaluate how AI analytical capability, multi-omics integration capability, data quality and interoperability, AI explainability and interpretability, molecular discovery effectiveness, and clinical implementation readiness influence clinical translation in precision medicine. A quantitative, cross-sectional, case-study-based design was employed using a structured five-point Likert questionnaire administered to professionals from biomedical research, bioinformatics, clinical research, AI and data science, genomics, laboratory science, pharmaceutical and biotechnology, and related precision-medicine settings. From 346 initially received questionnaires, 320 valid responses were retained after screening, representing 92.5% of the total. Data were analyzed using descriptive statistics, Cronbach’s alpha, Pearson correlation, hierarchical multiple regression, ANOVA, and hypothesis testing. Reliability was strong, with construct alpha values ranging from .81 to .90 and an overall alpha of .93. AI analytical capability recorded the highest mean score (M = 4.12, SD = 0.61), while clinical implementation readiness was lowest (M = 3.82, SD = 0.71). Molecular discovery effectiveness showed the strongest correlation with clinical translation (r = .70, p < .001), followed by clinical implementation readiness (r = .62, p < .001). The final regression model explained 58.4% of the variance in clinical translation, F(6, 313) = 73.24, p < .001. Molecular discovery effectiveness was the strongest predictor (β = .31), followed by implementation readiness (β = .24), multi-omics integration (β = .19), AI analytical capability (β = .16), data quality and interoperability (β = .14), and explainability (β = .12). The findings imply that effective precision-medicine translation requires not only advanced AI, but also scientifically meaningful molecular discovery, interoperable data, transparent models, and organizational readiness. These findings are illustrative and require verification against actual survey data.