Liquid biopsy has evolved as a transformative strategy revolutionizing the oncology field. It encompasses the detection of circulating biomarkers derived from tumors including circulating tumor DNA, extracellular vesicles, tumor educated platelets, circulating tumor cells, and circulating RNAs within body fluids. Nonetheless, its application in the clinical settings continues to be constrained by issues related to limited sensitivity, specificity, and lack of standardization. This review uniquely examines the convergence of artificial intelligence (AI) and machine learning (ML) algorithms with liquid biopsy to overcome these barriers and advance precision oncology. Focusing on four major malignancies; breast, lung, colorectal cancers, and hepatocellular carcinoma; we critically evaluate how AI-powered liquid biopsy improves early detection of cancer, prognosis prediction, and monitoring treatment response, while also forecasting recurrence and enabling patient stratification. We further highlight emerging algorithmic innovations, translational challenges, and ethical considerations, emphasizing the urgent need for harmonized validation frameworks to ensure reproducibility and clinical adoption. By leveraging AI-driven molecular insights, liquid biopsy can transition from a research concept to a routine clinical assay, enabling individualized therapeutic strategies, improving long-term survival, and ultimately transforming cancer care into a more predictive, personalized, and patient-centered paradigm.
AbdelHamid et al. (2026) studied this question.