MINI REVIEW article
Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
This article is part of the Research TopicAdvances in Electrochemical and Nanotechnology Point-of-Care Devices for Biosensing ApplicationsView all articles
Multianalyte Nano-biosensor Diagnostics: Advances through Microfluidic and AI Integration
Provisionally accepted
- Ghent University, Ghent, Belgium
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Recent advances in nano-biosensors are reshaping clinical diagnostics by enabling multiplexed biomarker detection with high sensitivity and precision. This mini-review examines both the opportunities and challenges in translating nano-biosensor technologies toward clinically relevant point-of-care (PoC) and wearable devices. We emphasize the integration of multiplexing strategies with microfluidic platforms and adaptive artificial intelligence (AI) algorithms, which together enable real-time, high-throughput, and personalized health monitoring. Electrochemical and optical transduction approaches for multi-biomarker diagnostics are discussed, along with the role of microfluidic integration in enhancing sensor performance through precise sample processing, reduced reagent use, and simultaneous biomarker detection. A comparative overview of multiplexing approaches, including spatial, spectral, and temporal encoding is presented, with particular attention to sensor surface regeneration for device reusability. Furthermore, we explore the role of adaptive AI algorithms in individualising diagnostics to diverse patient groups while addressing key ethical and regulatory considerations such as algorithm transparency, patient data protection, and compliance with evolving medical device standards. By drawing together insights across nano-biosensor design, microfluidics, and AI, this mini review provides practical guidance for advancing next-generation diagnostic platforms toward clinical translation.
Keywords: AI-enhanced diagnostics, Electrochemical-Optical Nano-biosensors, Ethical considerations in AI and biosensing, microfluidic, Multi-analyte detection, Sensorregeneration
Received: 29 Sep 2025; Accepted: 17 Dec 2025.
Copyright: © 2025 Pathak, Bazazordeh, Çamlıca and Tzouvadaki. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ioulia Tzouvadaki
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