Ther Clin Risk Manag. 2026 Oct 27;22:612081. doi: 10.2147/TCRM.S612081. eCollection 2026.
ABSTRACT
Circulating biomarkers, particularly cell-free DNA (cfDNA), are transforming contemporary medicine through their capacity to provide dynamic, minimally invasive assessments of disease dynamics. Their clinical utility has broadened substantially to include oncology, cardiovascular conditions, transplant monitoring, infectious diseases, and persistent inflammatory disorders. Nevertheless, the increasing complexity and volume of biomarker-related data pose substantial challenges, necessitating the integration of artificial intelligence (AI) and machine learning methodologies for improved data interpretation, risk assessment, and clinical decision-making. This review presents a thorough examination of recent progress in the integration of circulating biomarkers with AI within therapeutic contexts and clinical risk management. It outlines the translational trajectory from molecular signatures to precision medicine, addresses present constraints, and explores prospective developments. The objective of this review is to serve as a systematic resource for both clinical practice and research, thereby promoting the evolution of personalized medicine through innovative, biomarker-driven strategies supported by AI.
PMID:42967126 | PMC:PMC13723182 | DOI:10.2147/TCRM.S612081