CURRENT ISSUE

ABSTRACT: Cancer behaves as a dynamic evolutionary system, whereas conventional tissue biopsy and imaging provide incomplete temporal information about how a tumour is changing. This review critically examines whether longitudinal liquid-biopsy measurement, combined with artificial intelligence (AI), can move oncology from tumour monitoring toward genuine prediction of cancer evolution and treatment resistance. We synthesise mechanistic, clinical, and computational evidence on circulating tumour DNA, circulating tumour cells, extracellular vesicles, and multimodal liquid-biopsy platforms, together with AI approaches applied to longitudinal cancer data, drawing on landmark and recent studies across colorectal, lung, breast, and ovarian cancer. Current evidence distinguishes three increasingly demanding tasks: early detection, longitudinal monitoring, and genuine forecasting. Longitudinal circulating tumour DNA analysis can detect molecular progression and emerging resistance mutations months before radiographic progression, and machine-learning models can stratify survival using serial biomarker metrics; however, most published "AI prediction" studies demonstrate earlier detection or prognostic association rather than prospectively validated forecasting of a future, patient-specific event. Biological variability in tumour shedding, analytical noise, small and irregularly sampled longitudinal datasets, overfitting, data leakage, weak external validation, and limited interpretability constrain the transition from detection to actionable prediction. AI-enabled liquid biopsy has genuine potential to forecast tumour trajectories, but current evidence is considerably stronger for molecular monitoring and early detection than for prospectively validated, clinically actionable prediction. Realising this potential will require standardised assays, prospective longitudinal cohorts with predefined prediction horizons, rigorous external and temporal validation, calibrated and interpretable models, and interventional trials linking predictions to treatment decisions before AI-enabled liquid biopsy can be considered ready to guide clinical care.