Recent Advances in Artificial Intelligence (AI)-Enabled Biosensing Technologies

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

Deadline for manuscript submissions: 1 September 2026 | Viewed by 1037

Special Issue Editor


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Guest Editor
e-Health Electronics Center, Institute of Microelectronics of the Chinese Academy of Sciences, Beijing 100029, China
Interests: MEMS, micro/nano sensors, actuators, and systems; microfluidics and lab on a chip; biomedical microdevices and systems; BT-IT; semiconductor chips for life science and healthcare
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Special Issue Information

Dear Colleagues,

This Special Issue, entitled “Recent Advances in Artificial Intelligence (AI)-Enabled Biosensing Technologies”, is focused on the rapidly expanding interdisciplinary frontier that integrates artificial intelligence with biosensing technologies. This Special Issue’s scope includes, but is not limited to, AI-empowered biosensor design and fabrication, intelligent microfluidics, biosensing data processing, and smart systems, showcasing the transformative impact of AI for the science paradigm on next-generation biosensing technologies. Specifically, it encompasses the following:

  1. AI-based novel biosensor design methodologies and device implementation, optimization, and application;
  2. AI-powered biosensor data processing, multimodal fusion, predictive analytics, and insight generation;
  3. AI-driven adaptive and personalized systems, brain–computer interfaces, and neuromodulation;
  4. Biosensor-integrated robotic and automated intelligent systems and platform development and application;
  5. Biosensors integrated with neural network algorithms and/or neuromorphic chips for high-performance biosensing applications;
  6. Digital microfluidics with AI-based algorithms for intelligent droplet manipulation and applications.

We welcome contributions on AI-driven innovations across various biosensor types (e.g., wearable, implantable, microfluidics, point-of-care biosensors, and robotics) and integrated sensing systems. The emphasis is on leveraging AI to significantly improve performance in sensitivity, specificity, real-time analysis, and adaptability, thereby creating impactful applications in the medical, neuroscience, environmental monitoring, and personal healthcare fields, addressing challenges of global significance.

Prof. Dr. Chengjun Huang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Biosensors is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI empowered biosensing
  • intelligent biosensor design
  • predictive analytics
  • brain–computer interfaces
  • multimodal data fusion
  • AI for science

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Published Papers (1 paper)

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Review

29 pages, 2691 KB  
Review
Non-Invasive Urine-Based Diagnostic Technologies for Early Bladder Cancer
by Zhe Hao, Shuhua Yue, Lin Yao, Yanqing Gong, Jian Yu and Liqun Zhou
Biosensors 2026, 16(3), 171; https://doi.org/10.3390/bios16030171 - 20 Mar 2026
Viewed by 799
Abstract
Bladder cancer (BCa) is a major global urinary tract malignancy characterized by high incidence, frequent recurrence, and significant mortality. Early diagnosis is crucial for improving prognosis and minimizing invasive procedures; however, current standard techniques, cystoscopy and urine cytology, are limited by invasiveness, cost, [...] Read more.
Bladder cancer (BCa) is a major global urinary tract malignancy characterized by high incidence, frequent recurrence, and significant mortality. Early diagnosis is crucial for improving prognosis and minimizing invasive procedures; however, current standard techniques, cystoscopy and urine cytology, are limited by invasiveness, cost, low sensitivity, and subjectivity. This has spurred the development of non-invasive diagnostic strategies based on urine analysis. This review highlights five emerging approaches: AI-augmented urine cytology, genomic biomarker assays (e.g., PCR and NGS for mutations and copy-number variations), DNA methylation profiling, RNA biomarkers (mRNA, miRNA, lncRNA), and protein/peptide/metabolite detection utilizing ELISA, SERS, nanozymes, and mass spectrometry. We assess the diagnostic accuracy, innovations, and clinical potential of each, while addressing persisting issues such as lack of standardization, high costs, and insufficient sensitivity for early-stage lesions. Future directions include integrating multi-omics data with AI, advancing point-of-care devices, and conducting large-scale multicenter trials. Together, these developments promise to shift BCa management toward molecular-based early detection, enabling more precise, non-invasive, and personalized patient care. Full article
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