AI-Driven Biosensing

A special issue of Biosensors (ISSN 2079-6374).

Deadline for manuscript submissions: 30 April 2026 | Viewed by 955

Special Issue Editor


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Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: micro/nano-probes; smart biosensor; medical artificial intelligence; Internet of medical things

Special Issue Information

Dear Colleagues,

AI-driven biosensors represent a rapidly advancing frontier in the integration of artificial intelligence with biosensing technologies, offering unprecedented capabilities for intelligent signal interpretation, real-time anomaly detection, and predictive diagnostics. By incorporating advanced machine learning algorithms and data analytics, these systems enable the analysis of multidimensional biosensor data, improving the accuracy, robustness, and scalability of biosignal interpretation. The application of both supervised and unsupervised learning techniques facilitates the high-performance detection of complex biological targets, such as nucleic acids, proteins, and metabolites, driving biosensing technology toward smarter, adaptive, and autonomous systems. Through the combination of AI algorithms, machine learning models, and biosensor data analytics, the future of biosensing technologies is moving towards more intelligent, responsive, and autonomous systems with significant implications for clinical diagnostics and therapeutic interventions.

This Special Issue aims to bring together the latest developments in the design, integration, and application of AI-driven biosensors, highlighting their transformative potential for advanced diagnostics, personalized medicine, and real-time health monitoring. We welcome original research and review articles on AI-driven biosensors.

Dr. Jiuchuan Guo
Guest Editor

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Keywords

  • AI-driven biosensors
  • signal processing
  • transformative biosensors
  • point-of-care testing

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Published Papers (1 paper)

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Research

19 pages, 6255 KB  
Article
Double-Layer Simplified Complex Interval Neural Network Stacking for Blood Glucose Prediction of Continuous Glucose Monitoring System
by Shaowei Kong, Yusheng Fu, Jingshan Duan and Jian Yan
Biosensors 2025, 15(11), 707; https://doi.org/10.3390/bios15110707 - 22 Oct 2025
Viewed by 598
Abstract
Diabetes is a metabolic disorder characterized by persistent hyperglycemia, with its incidence steadily rising worldwide. Blood glucose monitoring is a core measure in diabetes management, and continuous glucose monitoring provides more comprehensive and accurate glucose data compared to traditional fingerstick testing. To collect [...] Read more.
Diabetes is a metabolic disorder characterized by persistent hyperglycemia, with its incidence steadily rising worldwide. Blood glucose monitoring is a core measure in diabetes management, and continuous glucose monitoring provides more comprehensive and accurate glucose data compared to traditional fingerstick testing. To collect continuous glucose data from patients, precise glucose prediction algorithms can help them better control their blood glucose fluctuations. Therefore, by addressing the issues of low prediction accuracy, complex input features, and poor generalization performance in existing glucose prediction methods, this paper proposes a glucose prediction model based on a double-layer SCINet stack using time-series analysis methods. SCINet effectively captures multi-scale dynamic features in time-series data through recursive down-sampling and convolution operations, making it suitable for glucose prediction tasks. Experimental data were sourced from real-world continuous glucose monitoring records of patients at Yixing People’s Hospital. Model input features were optimized through variable selection and data preprocessing, with predictive performance validated on a test dataset. The results demonstrate that the proposed model outperforms existing time-series prediction models across varying prediction horizons and patient datasets, exhibiting high predictive accuracy and stability. Full article
(This article belongs to the Special Issue AI-Driven Biosensing)
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