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.