Organ-on-a-chip (OoC) technologies, also termed microphysiological systems (MPSs), integrate microfluidics, engineered biomaterials, human-derived cells, and on-chip biosensing to model human physiology in microscale devices that deliver quantitative, time-resolved readouts. This review surveys the 2010–2025 literature, emphasizing how sensing, standardized sampling, and analytics enable clinical concordance and fit-for-purpose regulatory use. We synthesize advances in (i) materials, fabrication, and microfluidic design; (ii) organ- and disease-focused case studies; and (iii) translational benchmarks that align chip outputs with clinical pharmacokinetics, toxicology, and biomarker datasets. Across organ systems, platforms increasingly incorporate vascularization, immune components, and organoid hybrids, paired with real-time measurements of barrier integrity, metabolism, electrophysiology, and secreted biomarkers using impedance (TEER), electrochemical, and optical modalities. Representative benchmarking studies report cardiac OoCs achieving AUROC ≥ 0.85 for torsadogenic risk classification, and renal chips improving prediction of transporter-mediated clearance relative to conventional in vitro assays. We summarize validation approaches and regulatory developments relevant to new approach methodologies, including the FDA Modernization Act 2.0, and discuss how AI and multi-omics can automate signal and image analysis, harmonize cross-platform datasets, and support digital-twin workflows that couple OoC measurements to in silico models. Overall, biosensor-enabled OoCs are progressing toward quantitatively benchmarked platforms for safety pharmacology, ADME/PK–PD, and precision medicine.

Organ-on-a-Chip (OOC) technologies, also termed microphysiological systems (MPS), integrate microfluidics, engineered biomaterials, human-derived cells, and on-chip biosensing to model human physiology in microscale devices that deliver quantitative, time-resolved readouts. This review surveys the 2010–2025 literature, emphasizing how sensing, standardized sampling, and analytics enable clinical concordance and fit-for-purpose regulatory use. We synthesize advances in (i) materials, fabrication, and microfluidic design; (ii) organ- and disease-focused case studies; and (iii) translational benchmarks that align chip outputs with clinical pharmacokinetics, toxicology, and biomarker datasets. Across organ systems, platforms increasingly incorporate vascularization, immune components, and organoid hybrids, paired with real-time measurements of barrier integrity, metabolism, electrophysiology, and secreted biomarkers using impedance (TEER), electrochemical, and optical modalities. Representative benchmarking studies report cardiac OOCs achieving AUROC ≥0.85 for torsadogenic risk classification and renal chips improving prediction of transporter-mediated clearance relative to conventional in vitro assays. We summarize validation approaches and regulatory developments relevant to new approach methodologies, including the FDA Modernization Act 2.0, and discuss how AI and multi-omics can automate signal and image analysis, harmonize cross-platform datasets, and support digital-twin workflows that couple OOC measurements to in silico models. Overall, biosensor-enabled OOCs are progressing toward quantitatively benchmarked platforms for safety pharmacology, ADME/PK–PD, and precision medicine.

Biosensors are evolving from isolated analytical detectors into integrated systems for molecular diagnosis, longitudinal monitoring, dynamic tissue assessment, and clinical or preclinical decision support. This critical narrative review synthesizes literature from 2018 through July 2026 on biorecognition, biointerface engineering, transduction, wearable and microneedle architectures, CRISPR diagnostics, and sensor-integrated microphysiological systems. Unlike previous work that primarily classifies biosensors by analyte, recognition chemistry, or transduction modality, this review provides a unified translational framework that shifts evaluation from analytical sensitivity alone to the ability of systems to generate reliable, longitudinal, and clinically actionable information. A central distinction is made among snapshot assays, repeated discrete measurements, and genuine molecular trajectories, which require reversible recognition, controlled sampling, calibration, drift management, and temporal fidelity. Across applications, translational maturity depends on the measurement pathway. Continuous glucose monitoring remains the clearest benchmark because it combines durable chemistry, reproducible manufacture, workflow integration, and demonstrated clinical benefit. Other platforms have reached authentic-matrix testing, feasibility, prospective validation, or regulatory clearance but remain limited by fouling, matrix effects, calibration transfer, incomplete sample-to-answer operation, device variability, and insufficient manufacturing evidence. Future progress requires durable interfaces, claim-matched validation, reproducible scale-up, interoperable data systems, and measurements that remain trustworthy across time, users, devices, and settings.

Biosensors are evolving from isolated analytical detectors into integrated systems for molecular diagnosis, longitudinal monitoring, dynamic tissue assessment, and clinical or preclinical decision support. This critical narrative review synthesizes literature from 2018 through July 2026 on biorecognition, biointerface engineering, transduction, wearable and microneedle architectures, CRISPR diagnostics, and sensor-integrated microphysiological systems. Unlike previous work that primarily classifies biosensors by analyte, recognition chemistry, or transduction modality, this review provides a unified translational framework that shifts evaluation from analytical sensitivity alone to the ability of systems to generate reliable, longitudinal, and clinically actionable information. A central distinction is made among snapshot assays, repeated discrete measurements, and genuine molecular trajectories, which require reversible recognition, controlled sampling, calibration, drift management, and temporal fidelity. Across applications, translational maturity depends on the measurement pathway. Continuous glucose monitoring remains the clearest benchmark because it combines durable chemistry, reproducible manufacture, workflow integration, and demonstrated clinical benefit. Other platforms have reached authentic-matrix testing, feasibility, prospective validation, or regulatory clearance but remain limited by fouling, matrix effects, calibration transfer, incomplete sample-to-answer operation, device variability, and insufficient manufacturing evidence. Future progress requires durable interfaces, claim-matched validation, reproducible scale-up, interoperable data systems, and measurements that remain trustworthy across time, users, devices, and settings.