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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

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 9279

Special Issue Editors

Dr. Fabrizio Stasolla

E-Mail Website
Guest Editor
Developmental Psychology, "Giustino Fortunato" University of Benevento, 82100 Benevento, Italy
Interests: autism spectrum disorders; cerebral palsy; rare genetic syndromes (e.g., Angelman, Rett, Cornelia de Lange, fragile X); cognitive-behavioral interventions; post-coma; Alzeimer; Parkinson; sclerosis neurodegenerative diseases; single-subject experimental designs
Dr. Everardo Inzunza-González

E-Mail Website
Guest Editor
Faculty of Engineering, Architecture and Design, Universidad Autónoma de Baja California, Ensenada 22860, Baja California, Mexico
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

Special Issue Information

Dear Colleagues,

Integrating artificial intelligence (AI) and deep learning (DL) techniques into medical and assistive technology (AT) is revolutionizing the healthcare landscape, offering unprecedented precision and efficiency in diagnosing, monitoring, and treating various conditions. As the demand for personalized and accessible healthcare grows, these technologies are crucial in overcoming the limitations of traditional methods, providing new avenues for innovation in patient care. AI and deep learning can allow healthcare systems to handle vast numbers of data, enabling more accurate and timely interventions, which are vital in developing assistive technologies that enhance the quality of life for individuals with disabilities. These advancements not only improve existing medical practices but also drive the creation of novel tools and systems to reshape the future of healthcare.

For this Special Issue, we will gather new research at the intersection of AI, deep learning (DL), and biomedical engineering, redefining modern healthcare by showcasing innovative methods for diagnosing, monitoring, and treating various medical conditions. The focus will be on cutting-edge developments in AI and DL applications within medical technologies and assistive devices, addressing challenges in designing and deploying AI-driven solutions across healthcare domains.

Contributions are invited on topics such as the following:

  • AI and DL in medical diagnostics for early disease detection through medical imaging or signal processing;
  • 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;
  • the application of deep learning in biomedical imaging and signal interpretation;
  • smart medical device innovation for improved patient care, including security for telemedicine technologies;
  • the use of AI in personalized medicine for tailored treatment plans;
  • the integration of AI into IoT in healthcare environments to optimize patient outcomes;
  • the ethical and security challenges in AI-driven healthcare systems.

To conclude, we welcome submissions exploring the integration of AI-based programs and AT tools or devices into reinforcement learning principles and new technologies (e.g., augmented reality, virtual reality, serious games, and telerehabilitation) for both assessment and recovery purposes, to provide participants with highly customized and tailored technological solutions. This Special Issue will feature work that presents novel systems or approaches, frameworks, methods, algorithms, or applications, pushing the boundaries of what AI and DL can achieve in the healthcare sector.

Dr. Fabrizio Stasolla
Dr. Everardo Inzunza-González
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. 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 1600 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 (6 papers)

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22 pages, 7716 KiB  
Article
A Deep-Learning Approach to Heart Sound Classification Based on Combined Time-Frequency Representations
by Leonel Orozco-Reyes, Miguel A. Alonso-Arévalo, Eloísa García-Canseco, Roilhi F. Ibarra-Hernández and Roberto Conte-Galván
Technologies 2025, 13(4), 147; https://doi.org/10.3390/technologies13040147 - 7 Apr 2025
Viewed by 369
Abstract
Worldwide, heart disease is the leading cause of mortality. Cardiac auscultation, when conducted by a trained professional, is a non-invasive, cost-effective, and readily available method for the initial assessment of cardiac health. Automated heart sound analysis offers a promising and accessible approach to [...] Read more.
Worldwide, heart disease is the leading cause of mortality. Cardiac auscultation, when conducted by a trained professional, is a non-invasive, cost-effective, and readily available method for the initial assessment of cardiac health. Automated heart sound analysis offers a promising and accessible approach to supporting cardiac diagnosis. This work introduces a novel method for classifying heart sounds as normal or abnormal by leveraging time-frequency representations. Our approach combines three distinct time-frequency representations—short-time Fourier transform (STFT), mel-scale spectrogram, and wavelet synchrosqueezed transform (WSST)—to create images that enhance classification performance. These images are used to train five convolutional neural networks (CNNs): AlexNet, VGG-16, ResNet50, a CNN specialized in STFT images, and our proposed CNN model. The method was trained and tested using three public heart sound datasets: PhysioNet/CinC Challenge 2016, CirCor DigiScope Phonocardiogram Dataset 2022, and another open database. While individual representations achieve maximum accuracy of ≈85.9%, combining STFT, mel, and WSST boosts accuracy to ≈99%. By integrating complementary time-frequency features, our approach demonstrates robust heart sound analysis, achieving consistent classification performance across diverse CNN architectures, thus ensuring reliability and generalizability. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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28 pages, 3613 KiB  
Article
Chatbot Based on Large Language Model to Improve Adherence to Exercise-Based Treatment in People with Knee Osteoarthritis: System Development
by Humberto Farías, Joaquín González Aroca and Daniel Ortiz
Technologies 2025, 13(4), 140; https://doi.org/10.3390/technologies13040140 - 4 Apr 2025
Viewed by 453
Abstract
Knee osteoarthritis (KOA) is a prevalent condition globally, leading to significant pain and disability, particularly in individuals over the age of 40. While exercise has been shown to reduce symptoms and improve physical function and quality of life in patients with KOA, long-term [...] Read more.
Knee osteoarthritis (KOA) is a prevalent condition globally, leading to significant pain and disability, particularly in individuals over the age of 40. While exercise has been shown to reduce symptoms and improve physical function and quality of life in patients with KOA, long-term adherence to exercise programs remains a challenge due to the lack of ongoing support. To address this, a chatbot was developed using large language models (LLMs) to provide evidence-based guidance and promote adherence to treatment. A systematic review conducted under the PRISMA framework identified relevant clinical guidelines that served as the foundational knowledge base for the chatbot. The Mistral 7B model, optimized with Parameter-Efficient Fine-Tuning (PEFT) and Mixture-of-Experts (MoE) techniques, was integrated to ensure computational efficiency and mitigate hallucinations, a critical concern in medical applications. Additionally, the chatbot employs Self-Reflective Retrieval-Augmented Generation (SELF-RAG) combined with Chain of Thought (CoT) reasoning, enabling dynamic query reformulation and the generation of accurate, evidence-based responses tailored to patient needs. The chatbot was evaluated by comparing pre- and post-improvement versions and against a reference model (ChatGPT), using metrics of accuracy, relevance, and consistency. The results demonstrated significant improvements in response quality and conversational coherence, emphasizing the potential of integrating advanced LLMs with retrieval and reasoning methods to address critical challenges in healthcare. This approach not only enhances treatment adherence but also strengthens patient–provider interactions in managing chronic conditions like KOA. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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17 pages, 1167 KiB  
Article
Preprocessing-Free Convolutional Neural Network Model for Arrhythmia Classification Using ECG Images
by Chotirose Prathom, Ryuhi Fukuda, Yuto Yokoyanagi and Yoshifumi Okada
Technologies 2025, 13(4), 128; https://doi.org/10.3390/technologies13040128 - 26 Mar 2025
Viewed by 294
Abstract
Arrhythmia, which is characterized by irregular heart rhythms, can lead to life-threatening conditions by disrupting the circulatory system. Thus, early arrhythmia detection is crucial for timely and appropriate patient treatment. Machine learning models have been developed to classify arrhythmia using electrocardiogram (ECG) data, [...] Read more.
Arrhythmia, which is characterized by irregular heart rhythms, can lead to life-threatening conditions by disrupting the circulatory system. Thus, early arrhythmia detection is crucial for timely and appropriate patient treatment. Machine learning models have been developed to classify arrhythmia using electrocardiogram (ECG) data, which effectively capture the patterns associated with different abnormalities and achieve high classification performance. However, these models face challenges in terms of input coverage and robustness against data imbalance issues. Typically, existing methods employ a single cardiac cycle as the input, possibly overlooking the intervals between cycles, potentially resulting in the loss of critical temporal information. In addition, limited samples for rare arrhythmia types restrict the involved model’s ability to effectively learn, frequently resulting in low classification accuracy. Furthermore, the classification performance of existing methods on unseen data is not satisfactory owing to insufficient generalizability. To address these limitations, this research proposes a convolutional neural network (CNN) model for arrhythmia classification that incorporates two specialized modules. First, the proposed model utilizes images of three consecutive cardiac cycles as the input to expand the learning scope. Second, we implement a focal loss (FL) function during model training to prioritize minority classes. The experimental results demonstrate that the proposed model outperforms existing methods without requiring data preprocessing. The integration of multicycle ECG images and the FL function substantially enhances the model’s ability to capture ECG patterns, particularly for minority classes. In addition, our model exhibits satisfactory classification performance on unseen data from new patients. These findings suggest that the proposed model is a promising tool for practical application in arrhythmia classification tasks. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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28 pages, 3337 KiB  
Article
Lung and Colon Cancer Classification Using Multiscale Deep Features Integration of Compact Convolutional Neural Networks and Feature Selection
by Omneya Attallah
Technologies 2025, 13(2), 54; https://doi.org/10.3390/technologies13020054 - 1 Feb 2025
Cited by 1 | Viewed by 1495
Abstract
The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and [...] Read more.
The automated and precise classification of lung and colon cancer from histopathological photos continues to pose a significant challenge in medical diagnosis, as current computer-aided diagnosis (CAD) systems are frequently constrained by their dependence on singular deep learning architectures, elevated computational complexity, and their ineffectiveness in utilising multiscale features. To this end, the present research introduces a CAD system that integrates several lightweight convolutional neural networks (CNNs) with dual-layer feature extraction and feature selection to overcome the aforementioned constraints. Initially, it extracts deep attributes from two separate layers (pooling and fully connected) of three pre-trained CNNs (MobileNet, ResNet-18, and EfficientNetB0). Second, the system uses the benefits of canonical correlation analysis for dimensionality reduction in pooling layer attributes to reduce complexity. In addition, it integrates the dual-layer features to encapsulate both high- and low-level representations. Finally, to benefit from multiple deep network architectures while reducing classification complexity, the proposed CAD merges dual deep layer variables of the three CNNs and then applies the analysis of variance (ANOVA) and Chi-Squared for the selection of the most discriminative features from the integrated CNN architectures. The CAD is assessed on the LC25000 dataset leveraging eight distinct classifiers, encompassing various Support Vector Machine (SVM) variants, Decision Trees, Linear Discriminant Analysis, and k-nearest neighbours. The experimental results exhibited outstanding performance, attaining 99.8% classification accuracy with cubic SVM classifiers employing merely 50 ANOVA-selected features, exceeding the performance of individual CNNs while markedly diminishing computational complexity. The framework’s capacity to sustain exceptional accuracy with a limited feature set renders it especially advantageous for clinical applications where diagnostic precision and efficiency are critical. These findings confirm the efficacy of the multi-CNN, multi-layer methodology in enhancing cancer classification precision while mitigating the computational constraints of current systems. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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20 pages, 3968 KiB  
Article
HybridFusionNet: Deep Learning for Multi-Stage Diabetic Retinopathy Detection
by Amar Shukla, Shamik Tiwari and Anurag Jain
Technologies 2024, 12(12), 256; https://doi.org/10.3390/technologies12120256 - 11 Dec 2024
Viewed by 1721
Abstract
Diabetic retinopathy (DR) is one of the most common causes of visual impairment worldwide and requires reliable automated detection methods. Numerous research efforts have developed various conventional methods for early detection of DR. Research in the field of DR remains insufficient, indicating the [...] Read more.
Diabetic retinopathy (DR) is one of the most common causes of visual impairment worldwide and requires reliable automated detection methods. Numerous research efforts have developed various conventional methods for early detection of DR. Research in the field of DR remains insufficient, indicating the potential for advances in diagnosis. In this paper, a hybrid model (HybridFusionNet) that integrates vision transformer (VIT) and attention processes is presented. It improves classification in the binary (Bcl) and multi-class (Mcl) stages by utilizing deep features from the DR stages. As a result, both the SAN and VIT models improve the recognition accuracy (Acc) in both stages.The HybridFusionNet mechanism achieves a competitive improvement in multi-stage and binary stages, which is Acc in Bcl and Mcl, with 91% and 99%, respectively. This illustrates that this model is suitable for a better diagnosis of DR. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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Other

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28 pages, 676 KiB  
Systematic Review
Challenges and Ethical Considerations in Implementing Assistive Technologies in Healthcare
by Eleni Gkiolnta, Debopriyo Roy and George F. Fragulis
Technologies 2025, 13(2), 48; https://doi.org/10.3390/technologies13020048 - 27 Jan 2025
Cited by 1 | Viewed by 3868
Abstract
Assistive technologies are becoming an increasingly important aspect of healthcare, particularly for people with physical or cognitive problems. While earlier research has investigated the ethical, legal, and societal implications of AI and assistive technologies, many studies have failed to address real-world obstacles such [...] Read more.
Assistive technologies are becoming an increasingly important aspect of healthcare, particularly for people with physical or cognitive problems. While earlier research has investigated the ethical, legal, and societal implications of AI and assistive technologies, many studies have failed to address real-world obstacles such as data privacy, algorithm bias, and regulatory issues. To further understand these issues, we conducted a thorough analysis of the current literature and analyzed real-world case studies. As AI-powered solutions become more widely used, we discovered that stronger legal frameworks and robust data security standards are required. Furthermore, privacy-preserving procedures and transparent accountability are critical for retaining patient trust and guaranteeing the effective use of these technologies in healthcare. This research provides important insights into the ethical and practical challenges that must be tackled for the successful integration of assistive technologies. Full article
(This article belongs to the Special Issue Advancements in Medical and Assistive Technologies Using Artificial Intelligence and Deep Learning Techniques)
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