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Omics technologies analyze comprehensive molecular datasets, enabling the detailed characterization of pathological processes. Genomic, transcriptomic, proteomic, and metabolomic approaches, as well as emerging single-cell and spatial omics, enhance biomarker discovery, early diagnosis, patient stratification, therapy selection, and prognosis. Multi-omics integration, combined with artificial intelligence (AI), could facilitate precision medicine by predicting treatment responses, uncovering disease heterogeneity, and defining personalized treatment. Clinical trials demonstrate the translational potential of omics across oncology, cardiovascular, neurological, and other conditions. Despite rapid technological progress, challenges remain, including data complexity, costs, standardization, and clinical interpretability. Ongoing efforts in guideline development, AI-assisted integration, and standardized workflows aim to bridge the gap between research and routine practice. This review highlights the potential clinical applications of omics and current guidelines for their implementation in routine practice.
Zamperla et al. (Wed,) studied this question.