Multimodal Biosensing for Liquid Biopsy: A Critical Analysis of Signal Complementarity, Data Fusion, Artificial Intelligence, and Clinical Translation

Liquid biopsy provides minimally invasive access to diverse tumor-derived biomarkers, but their low abundance, biological variability, and complex matrices limit the reliability of single-mode biosensing. Multimodal biosensors address these challenges by combining complementary sensing mechanisms; however, multiple signals do not necessarily constitute meaningful data fusion or clinical benefit. This review critically evaluates multimodal liquid-biopsy biosensing at the signal, information, and computational/decision levels, focusing on whether additional modalities provide non-redundant analytical information. Molecular recognition, functional materials, signal engineering, integrated architectures, and computational methods are assessed alongside analytical validation, matrix effects, reproducibility, clinical-sample evidence, external validation, and AI maturity. A complementary framework distinguishes analytical, clinical, computational, and deployment readiness. Overall, most platforms remain at proof-of-concept or early validation stages, with limited evidence of externally validated data fusion or clinical utility. The review identifies the conditions required for multimodality to deliver genuine analytical value and outlines the methodological, computational, and clinical requirements for advancing toward reliable liquid-biopsy decision systems.