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AI/ML in RF and Microwave Sensors for Medicine and Biomedical Applications

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1155

Special Issue Editors


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Guest Editor
Department of Computer Science, University of Wisconsin-Eau Claire, Eau Claire, WI 54701, USA
Interests: bioinformatics; deep learning; machine learning; biomedical image processing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
1. Microwave Engineering and Imaging Laboratory (MEIL), Division of Gastroenterology & Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA
2. Department of Computer Science and Computer Engineering, University of Wisconsin-La Crosse, La Crosse, WI 54601, USA
Interests: transformation optics/electromagnetics (TO/TE) and its applications for next-generation sensors and devices; engineered electromagnetic materials; RF/microwave devices for IoT and healthcare; machine learning for wireless communications and biomedical applications; 3D-printed flexible and wearable electronics for embedded systems and biomedical applications
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent advancements in RF and microwave devices have significantly enhanced their utility in medicine and biomedical applications, revolutionizing areas such as diagnostics, therapeutic systems, and medical imaging. With the growing integration of artificial intelligence (AI), deep learning, reinforcement learning, and emerging technologies like explainable AI and federated learning, there is a tremendous opportunity to drive innovation and tackle challenges in this interdisciplinary domain.

This Special Issue aims to compile original research and review articles focusing on cutting-edge developments, solutions, and applications of RF and microwave technologies in medicine, emphasizing the transformative role of AI-powered methodologies.

We encourage submissions exploring theoretical innovations, experimental validations, and interdisciplinary approaches combining RF technologies with advanced AI techniques. By addressing these topics, this Special Issue aims to highlight the latest advancements and foster collaboration across fields to improve medical outcomes and patient care.

Dr. Rahul Gomes
Dr. Dipankar Mitra
Guest Editors

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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors is an international peer-reviewed open access semimonthly 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 2600 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-driven advancements in RF and microwave medical imaging
  • microwave-based non-invasive diagnostics and therapeutic systems
  • explainable AI for RF and microwave healthcare applications
  • federated learning for distributed medical data analysis using RF systems
  • deep learning in microwave hyperthermia and ablation therapies
  • integration of RF devices with biomedical sensors for real-time monitoring
  • reinforcement learning in adaptive RF-based treatment protocols
  • RF and microwave devices for wearable and implantable medical systems
  • AI-powered microwave imaging for tumor detection and monitoring
  • RF-based solutions for point-of-care diagnostics
  • challenges and solutions in the standardization and safety of RF devices in medicine

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Published Papers (1 paper)

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Review

30 pages, 1745 KiB  
Review
The Human Voice as a Digital Health Solution Leveraging Artificial Intelligence
by Pratyusha Muddaloor, Bhavana Baraskar, Hriday Shah, Keerthy Gopalakrishnan, Divyanshi Sood, Prem C. Pasupuleti, Akshay Singh, Dipankar Mitra, Sumedh S. Hoskote, Vivek N. Iyer, Scott A. Helgeson and Shivaram P. Arunachalam
Sensors 2025, 25(11), 3424; https://doi.org/10.3390/s25113424 - 29 May 2025
Viewed by 937
Abstract
The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is [...] Read more.
The human voice is an important medium of communication and expression of feelings or thoughts. Disruption in the regulatory systems of the human voice can be analyzed and used as a diagnostic tool, labeling voice as a potential “biomarker”. Conversational artificial intelligence is at the core of voice-powered technologies, enabling intelligent interactions between machines. Due to its richness and availability, voice can be leveraged for predictive analytics and enhanced healthcare insights. Utilizing this idea, we reviewed artificial intelligence (AI) models that have executed vocal analysis and their outcomes. Recordings undergo extraction of useful vocal features to be analyzed by neural networks and machine learning models. Studies reveal machine learning models to be superior to spectral analysis in dynamically combining the huge amount of data of vocal features. Clinical applications of a vocal biomarker exist in neurological diseases such as Parkinson’s, Alzheimer’s, psychological disorders, DM, CHF, CAD, aspiration, GERD, and pulmonary diseases, including COVID-19. The primary ethical challenge when incorporating voice as a diagnostic tool is that of privacy and security. To eliminate this, encryption methods exist to convert patient-identifiable vocal data into a more secure, private nature. Advancements in AI have expanded the capabilities and future potential of voice as a digital health solution. Full article
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