Integrating multi-omic liquid biopsies and artificial intelligence: The next frontier in early cancer detection

The integration of multi-omic liquid biopsies with artificial intelligence (AI) represents a rapidly evolving frontier in early cancer detection, offering the potential to enhance personalized medicine and improve patient outcomes. This review explores the current state and emerging directions of this approach, focusing on the synergistic value of combining genomics, epigenomics, transcriptomics, proteomics, and metabolomics with AI-driven analytics. We discuss advances in multi-analyte blood tests such as CancerSEEK, which have demonstrated promising multi-cancer detection capabilities in early studies, as well as efforts to integrate liquid biopsy data with imaging modalities to improve diagnostic performance. The review also highlights ongoing challenges, including the need for greater analytical sensitivity, improved specificity for early-stage disease, standardization of workflows, and harmonization with existing screening modalities. We outline the prospective—but still largely investigational—impact of these technologies on cancer management, including early detection, treatment monitoring, and minimal residual disease assessment, along with their potential economic implications. Ultimately, we envision a future in which multi-omic liquid biopsies integrated with AI may contribute to more effective, noninvasive cancer detection strategies, while recognizing that substantial validation, regulatory approval, and health-system integration are required before widespread clinical adoption can occur.