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Technologies
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Advancements in Medical and Assistive Technologies Using Artificial Intelligence and...
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Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques—2nd Edition

A special issue of Technologies (ISSN 2227-7080). This special issue belongs to the section "Assistive Technologies".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 965

Editors

Dr. Everardo Inzunza-González

E-Mail Website
Guest Editor
Faculty of Engineering, Architecture and Design, Universidad Autónoma de Baja California, Ensenada 22860, BCN, México
Interests: artificial intelligence; data science; medical imaging; biomedical signal processing; machine learning; deep learning; IoT; H-IoT; network security; wearable devices; embedded systems
Special Issues, Collections and Topics in MDPI journals
Dr. Fabrizio Stasolla

E-Mail Website
Guest Editor
1. Developmental Psychology, "Giustino Fortunato" University of Benevento, 82100 Benevento, Italy
2. Faculty of Law, Giustino Fortunato University, 82100 Benevento, Italy
Interests: virtual reality; assistive technology; cognitive-behavioral approach; ADHD (attention deficit and hyperactivity disorder); ASD (autism spectrum disorders); single-subject design; rare diseases; augmentative and alternative communication; augmentative and alternative communication technologies; Alzheimer’s disease (AD); multiple sclerosis; multiple disabilities; clinical rehabilitation; neurodegenerative diseases; neurodevelopmental disorders; telerehabilitation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This second edition of “Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques" https://www.mdpi.com/journal/technologies/special_issues/62L241LE6H aims to build on the success of the first edition, which brought together 14 high-quality contributions spanning brain tumor segmentation in MRI, EEG-based motor imagery classification, automated heart sound analysis, arrhythmia detection from ECG signals, lung and colon cancer classification from histopathological images, diabetic retinopathy grading, LLM-based chatbots for rehabilitation adherence, ethical considerations in assistive technologies, and assistive robotics. With this Special Issue we seek to further advance the integration of artificial intelligence (AI), Machine Learning (ML), and deep learning (DL) techniques into medical and assistive technology (AT).

The convergence of AI, ML, and DL with biomedical applications continues to reshape the healthcare landscape, offering unprecedented precision and efficiency in diagnosing, monitoring, and treating a wide spectrum of conditions. As the demand for personalized, accessible, and equitable healthcare grows, these technologies play a pivotal role in overcoming the limitations of traditional methods, enabling healthcare systems to process vast, heterogeneous datasets to enable more accurate and timely clinical interventions. These advancements not only refine existing medical practices but also catalyze the development of novel tools and intelligent systems that are redefining the future of patient care and assistive solutions for individuals with disabilities.

For this second edition, we aim to gather cutting-edge research at the intersection of AI, ML, DL, and biomedical engineering, with an expanded thematic scope that reflects the field’s rapidly evolving landscape. In particular, this edition places special emphasis on the following emerging areas:

  • ML, DL, and AI in medical diagnostics for early disease detection and classification through biomedical imaging or signal processing.
  • Novel methods, frameworks, and techniques for augmented reality, virtual reality, serious games, gamification, and telerehabilitation.
  • The development of adaptive assistive technologies and robotics to support individuals with disabilities.
  • Advancements in healthcare monitoring systems powered by AI for real-time analysis.
  • AI-driven biomedical signal processing.
  • Smart medical device innovation for improved patient care, including security for telemedicine technologies.
  • The use of AI and LLM in personalized medicine for treatment plans.
  • The integration of AI and LLM into IoT in healthcare environments to optimize patient outcomes.
  • Federated learning, transfer learning, and other advanced learning paradigms applied to healthcare data.
  • The ethical, privacy and security challenges in AI-driven healthcare systems.

This Special Issue will feature work that presents novel systems, approaches, frameworks, methods, algorithms, or applications, pushing the boundaries of what AI, ML, and DL can achieve in the healthcare sector.

We welcome original research articles, comprehensive reviews, and short communications from researchers worldwide.

Dr. Everardo Inzunza-González
Dr. Fabrizio Stasolla
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 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Technologies 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 1800 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

  • artificial intelligence
  • medical imaging
  • machine learning
  • medical image classification
  • deep learning
  • H-IoT
  • biomedical signal processing
  • deep neural networks
  • CNNs
  • health informatics
  • computer-aided diagnosis
  • data science

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Published Papers (3 papers)

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19 pages, 6327 KB  
Article
Performance of an Efficient Hybrid Dilated–Long Short-Term Memory with Residual Learning for High-Fidelity Electrocardiogram Denoising Signal
by Suchada Sitjongsataporn, Pipat Sakarin and Theerayod Wiangtong
Technologies 2026, 14(7), 453; https://doi.org/10.3390/technologies14070453 (registering DOI) - 22 Jul 2026
Abstract
Addressing the critical challenge of signal degradation in biosensor cardiac monitoring, this paper introduces an efficient hybrid dilated–long short-term memory (LSTM) with residual learning (HDLR), which is a novel architecture engineered for a high-fidelity electrocardiogram (ECG) denoising signal. The proposed HDLR model synergistically [...] Read more.
Addressing the critical challenge of signal degradation in biosensor cardiac monitoring, this paper introduces an efficient hybrid dilated–long short-term memory (LSTM) with residual learning (HDLR), which is a novel architecture engineered for a high-fidelity electrocardiogram (ECG) denoising signal. The proposed HDLR model synergistically integrates with dilated convolutions to expand the receptive field for multi-scale feature extraction. This is an LSTM-based backbone used to resolve long term temporal dependencies with residual learning paths to stabilize gradient flow and accelerate convergence. The proposed HDLR architecture integrates three core functional components with dilated convolutional layers utilized for local temporal feature extraction, where varying dilation rates expand the receptive field to capture both local waveform patterns and broader morphological structures without increasing computational complexity. Experimental results demonstrate a significant leap in performance, with the HDLR model achieving a mean squared error (MSE) of 0.002176, a signal-to-noise ratio (SNR) of 14.4420 dB, and a Matthews correlation coefficient (MCC) of 0.9822. Beyond quantitative metrics, the proposed HDLR architecture exhibits exceptional robustness in preserving cardiac morphology, specifically the P-wave and QRS complex of the ECG signal under stochastic noise conditions. These findings underscore the HDLR model’s potential as a backbone for next generation, real time diagnostic systems in intelligent healthcare. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques—2nd Edition)
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26 pages, 825 KB  
Article
Deep Neural Network-Based Segmentation of Epileptiform Activity Patterns in EEG Approaches Inter-Expert Agreement for a Pediatric Test Cohort
by Nikolay V. Gromov, Albina V. Lebedeva, Artem A. Sharkov, Anna D. Grebenyukova, Oksana D. Elshina, Anastasiya M. Borisova, Valentin Yu. Borisov, Anton E. Malkov, Lev A. Smirnov, Tatiana A. Levanova and Alexander N. Pisarchik
Technologies 2026, 14(7), 403; https://doi.org/10.3390/technologies14070403 - 1 Jul 2026
Viewed by 335
Abstract
Automatic analysis of electroencephalography (EEG) recordings relies on large, high-quality labeled datasets. Manual segmentation by medical experts is resource-intensive and time-consuming. Moreover, to overcome potential subjectivity in labeling, independent annotation by at least two experts is required. Therefore, reliable automatic data labeling is [...] Read more.
Automatic analysis of electroencephalography (EEG) recordings relies on large, high-quality labeled datasets. Manual segmentation by medical experts is resource-intensive and time-consuming. Moreover, to overcome potential subjectivity in labeling, independent annotation by at least two experts is required. Therefore, reliable automatic data labeling is essential for obtaining the large datasets needed to train robust AI models. In this paper, we show that a properly trained state-of-the-art deep neural network (DNN) achieves labeling performance comparable to inter-expert agreement in the task of segmenting epileptiform activity patterns. To this end, we first compiled a custom database of EEG recordings containing such patterns. Second, five experts based on part of these recordings independently assessed spike-wave index (SWI), which is a key diagnostic criterion that indicates the percentage of the EEG recording during which epileptic discharges are observed. Third, we compared the expert assessments with SWI calculated based on automatic segmentation by the trained DNN. Our results demonstrate that the 1D U-Net architecture achieves competitive overall performance and aligns well with both expert assessments and expert-derived SWI values. Thus, automated segmentation and analysis of EEG recordings holds great promise for accelerating diagnosis and developing targeted therapeutic strategies for epilepsy. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques—2nd Edition)
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22 pages, 4661 KB  
Article
Foundation Time-Series Models for Local Forecasting in Adults with Type 1 Diabetes Using Continuous Glucose Monitoring Signals
by Roberto Carlos Diaz-Velazco, Alberto Gudiño-Ochoa, Julio Alberto García-Rodríguez, Jorge Ivan Cuevas-Chávez and Eduardo Ruiz-Velázquez
Technologies 2026, 14(7), 399; https://doi.org/10.3390/technologies14070399 - 30 Jun 2026
Viewed by 369
Abstract
Foundation time-series models have recently shown potential for forecasting complex temporal signals, but their behavior in patient-specific continuous glucose monitoring (CGM) forecasting remains insufficiently understood, particularly when only glucose history is available. This study provides a patient-level benchmark of foundation models for 30 [...] Read more.
Foundation time-series models have recently shown potential for forecasting complex temporal signals, but their behavior in patient-specific continuous glucose monitoring (CGM) forecasting remains insufficiently understood, particularly when only glucose history is available. This study provides a patient-level benchmark of foundation models for 30 min ahead glucose prediction in adults with type 1 diabetes mellitus (T1DM) under a strictly univariate CGM-only setting. Using the HUPA–UCM dataset from 25 individuals, we evaluated TimeGPT, Chronos, and Sundial against representative statistical, machine learning, and deep learning forecasters, including ARIMA, ETS, gradient-boosting models, recurrent networks, and neural forecasting architectures. Models were assessed using a local walk-forward validation strategy over the final 24 h of CGM data for each patient. Foundation models achieved the strongest global performance, with Sundial obtaining the lowest overall MAE (6.06mg/dL), while TimeGPT and Chronos remained among the most competitive approaches. However, patient-level analyses showed that this advantage was not uniform: ARIMA remained highly competitive in selected individuals, and no single model consistently dominated across the cohort. These findings suggest that foundation time-series models are promising tools for short-horizon CGM forecasting, but their use should be framed within patient-specific model selection rather than as universal replacements for classical forecasting methods. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques—2nd Edition)
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