AI-Integrated biosensors: A paradigm shift in multi-cancer detection with enhanced sensitivity and specificity

• Early-stage multi-cancer detection is a major challenge and needs reliable, low-burden tests. • Conventional diagnostics are invasive, slow, costly, and can lack accuracy for screening. • Integration of biosensors with artificial intelligence improves sensitivity at clinically acceptable specificity and enables tumor-of-origin and longitudinal monitoring. • Clinical translation requires standardized pre-analyses, calibrated reporting, multi-site validation, and strong data privacy safeguards. Cancer remains a major global health problem, with survival closely tied to early detection. Conventional methods like physical examination, laboratory tests, biopsy, and imaging have limitations for detecting low-abundance cancer biomarkers at earlier stages. Because early-stage cancer biomarkers are present at low abundance, there is a need for technologies with low limits of detection that are highly sensitive, specific, and achievable at a manageable cost. In this review, we performed a structured literature search of PubMed, Web of Science, and Google Scholar from database inception up to December 2024 using keywords related to biosensors, electrochemical, optical, nanomaterial, multi-cancer early detection, and artificial intelligence. We evaluate recent advances in biosensor-based strategies for minimally invasive sampling and summarize how electrochemical, optical, and nanomaterial-based biosensors convert biomarker interactions into measurable signals. For each modality, we outline transduction principles, key biomarker performance, and multiplex capacity, with applications spanning liquid biopsy, breath analysis, and tumor microenvironment readouts. We also summarize AI methods for denoising, feature extraction, multimodal fusion, and classifier training for cancer detection and tumor of origin assignment. Finally, we highlight key translational priorities, including standardized pre-analytical workflows, multi-site studies, well-curated datasets, transparent preprocessing, external validation, privacy-preserving training, and integration with clinical information systems. Overall, the reviewed literature indicates that AI most consistently improves interpretation and classification from complex biosensor readouts, but clinically reliable MCED is still limited by non-standardized pre-analytical handling and insufficient independent external validation. These findings support AI-integrated biosensors as a promising route to shift diagnosis toward earlier stages and enable efficient care pathways within a scalable MCED workflow. This review article describes the integration of advanced biosensors with artificial intelligence for multi-cancer early detection. It summarizes electrochemical, optical, and nanomaterial-based biosensors that analyze liquid biopsies, leveraging AI to denoise data, fuse modalities, and train classifiers. This synergistic approach aims to shift cancer diagnosis to earlier, more treatable stages, creating scalable and efficient clinical workflows for improved patient outcomes.