Development Trends of AI-Enabled Biomedical Biosensors

A special issue of Biosensors (ISSN 2079-6374). This special issue belongs to the section "Biosensors and Healthcare".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 4142

Special Issue Editors

School of Instrumentation and Optoelectronic Engineering, Beihang University, Beijing 100191, China
Interests: functional materials; wearable devices and systems; flexible electronics
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Guest Editor Assistant
Electrical Engineering Division, Department of Engineering, University of Cambridge, Cambridge, UK
Interests: wearable systems; sensors; artificial intelligence; healthcare; robotics

Special Issue Information

Dear Colleagues,

The rapid evolution of artificial intelligence (AI) is significantly transforming the field of biomedical biosensing, leading to innovative solutions for diagnostics and health monitoring.

AI-enabled biomedical biosensors leverage advanced algorithms to enhance the performance of traditional biosensing technologies. By improving the sensitivity and specificity of these sensors, AI facilitates the accurate detection of biomarkers associated with diseases, enabling earlier diagnoses and better patient outcomes. We propose a Special Issue titled "Development Trends of AI-Enabled Biomedical Biosensors" to explore the intersection of artificial intelligence (AI) and biosensing technologies in the biomedical field. As healthcare increasingly demands rapid and accurate diagnostic solutions, AI-enabled biosensors are emerging as a transformative tool for early disease detection and continuous health monitoring.

This Special Issue aims to gather cutting-edge research that highlights the latest advancements in the integration of AI with various biosensing modalities, including electrochemical, optical, and acoustic sensors. We seek contributions that address novel AI-enabled biosensors, machine learning applications for enhancing sensor accuracy and innovative designs of smart biosensors capable of real-time monitoring of biomarkers.

Dr. Shuo Gao
Guest Editor

Dr. Chenyu Tang
Guest Editor Assistant

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Keywords

  • biosensor
  • point-of-care diagnostics
  • AI/ML-based biosensor
  • flexible biosensor
  • lab-on-chip

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Published Papers (2 papers)

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Review

25 pages, 4009 KB  
Review
Evolution of Next-Generation Multiplex Lateral Flow Immunoassays: From Engineered Nanomaterials to AI-Driven Detection
by Tan-Thanh Huynh, Duc-Thang Vo and Trong-Nghia Le
Biosensors 2026, 16(5), 269; https://doi.org/10.3390/bios16050269 - 7 May 2026
Viewed by 648
Abstract
Decentralized diagnostics is undergoing a transformative shift from qualitative screening to high-precision quantification, driven by the clinical demand for rapid, point-of-care (POC) syndromic triage. Multiplexed lateral flow immunoassays (mLFIAs) serve as the foundational platform for this transition. However, their performance is limited by [...] Read more.
Decentralized diagnostics is undergoing a transformative shift from qualitative screening to high-precision quantification, driven by the clinical demand for rapid, point-of-care (POC) syndromic triage. Multiplexed lateral flow immunoassays (mLFIAs) serve as the foundational platform for this transition. However, their performance is limited by systemic factors such as fluidic lag, conjugate depletion, and spectral crosstalk. This review evaluates recent advances in engineered nanomaterials and artificial intelligence (AI)-driven detection as the dual pillars of next-generation multiplexing. The review covers different types of nanomaterial reporters—such as multicolor quantum dots, surface-enhanced Raman scattering nanotags, upconversion nanoparticles, surface-modified magnetic nanoparticles, and fluorescent nanodiamonds—that help address analytical challenges in lateral flow assays. We then discuss AI and machine learning methods, including convolutional neural networks, support vector machines, random forests, and transfer learning, that convert raw multi-channel signals into useful clinical data. Finally, we highlight the main challenges that still need to be addressed before these platforms can become WHO-ASSURED-compliant POC devices. The combination of engineered nanomaterial reporters and computational intelligence is transforming lateral flow assays into quantitative tools that can provide lab-quality clinical information at the POC. Full article
(This article belongs to the Special Issue Development Trends of AI-Enabled Biomedical Biosensors)
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60 pages, 12125 KB  
Review
Active Rehabilitation Technologies for Post-Stroke Patients
by Hongbei Meng, Zihe Zhao, Shangru Li, Shengbo Wang, Jiacheng Wang, Canxi Yang, Chenyu Tang, Xuhang Chen, Xiaoxue Zhai, Yu Pan, Arokia Nathan, Peter Smielewski, Luigi G. Occhipinti and Shuo Gao
Biosensors 2026, 16(1), 20; https://doi.org/10.3390/bios16010020 - 25 Dec 2025
Viewed by 3142
Abstract
Neuroplasticity-based active movement opens an avenue for functional recovery in post-stroke patients. Active rehabilitation techniques have attracted wide attention based on their abilities to enhance patient involvement, facilitate precise personalized intervention, and provide comprehensive treatment via cross-domain approaches. Emerging evidence suggests that active [...] Read more.
Neuroplasticity-based active movement opens an avenue for functional recovery in post-stroke patients. Active rehabilitation techniques have attracted wide attention based on their abilities to enhance patient involvement, facilitate precise personalized intervention, and provide comprehensive treatment via cross-domain approaches. Emerging evidence suggests that active rehabilitation methods can respond to patients’ motor intentions in real-time and significantly increase motivation and engagement, leading to efficient utilization of critical recovery windows and better rehabilitation outcomes. In this review, we focus on the physiological basis of active rehabilitation, including mechanisms of neuroplasticity, and discuss recent advances in intent detection and feedback devices. We also examine treatment options for different stages of stroke recovery, providing a comprehensive reference for engineers to design optimized rehabilitation techniques and for clinicians to select appropriate rehabilitation protocols. These developments create new opportunities to improve the lives of stroke patients and offer greater hope for their recovery. Full article
(This article belongs to the Special Issue Development Trends of AI-Enabled Biomedical Biosensors)
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