Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.1
2.6
Journals
Electronics
Special Issues
Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition
share announcement

Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 8764

Special Issue Editors

Dr. Athanasios Anastasiou

E-Mail Website
Guest Editor
Department of Telematics and Informatics, Harokopio University of Athens, Athens, Greece
Interests: bioinformatics; biosignal processing; decision support systems; activity recognition; telemedical technologies; digital image processing; technologies for assisted living; mHealth
Dr. Antonios N. Gargalionis

E-Mail Website
Guest Editor
Assistant Professor of Clinical Biochemistry and Medical Chemistry, Department of Clinical Biochemistry, School of Medicine, National and Kapodistrian University of Athens, 115 28 Athens, Greece
Interests: signal transduction; mechanobiology; tumorigenesis
Special Issues, Collections and Topics in MDPI journals
Dr. George Dimitrakopoulos

E-Mail Website
Guest Editor
Department of Informatics and Telematics, Harokopio University of Athens, 17676 Kallithea, Greece
Interests: AI-enabled algorithms for the optimization of communication networks; cognitive networks; intelligent transport systems; highly automated/autonomous driving
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This focus of this Special Issue on artificial intelligence (AI) technologies for biomedicine and healthcare applications is on cutting-edge AI methodologies and their transformative impact on medical and healthcare systems. Below are several potential areas of focus:

  1. AI in Medical Imaging and Diagnostics
  • Deep learning for imspaging: the utilization of convolutional neural networks (CNNs) and generative adversarial networks (GANs) for medical imaging analysis, including radiology, pathology, and dermatology.
  • Automated diagnostics: AI-driven tools for detecting and diagnosing diseases, such as cancer, heart disease, and neurological disorders, from medical images.
  • Explainability in AI diagnostics: Enhancing transparency and trust in AI algorithms used for critical healthcare decision making.
  1. AI in Precision Medicine and Personalized Healthcare
  • Genomic data analysis: AI applications in analyzing genomic data for drug discovery, disease predisposition, and personalized treatment plans.
  • Predictive analytics for patient outcomes: machine learning models that predict patient responses to treatments based on electronic health records (EHRs) and other data.
  • AI-driven drug discovery: leveraging AI for accelerating the discovery of new drugs and personalized therapeutic interventions.
  1. Natural Language Processing (NLP) in Healthcare
  • Clinical documentation automation: AI-driven NLP for processing unstructured clinical notes and improving documentation efficiency in EHR systems.
  • Speech recognition for medical transcription: AI systems to facilitate accurate real-time transcription for physicians and clinical personnel.
  • AI-powered chatbots and virtual health assistants: Using NLP to provide patient support, answer medical queries, and assist in telemedicine.
  1. AI in Digital Health and Telemedicine
  • Remote patient monitoring: AI-based platforms for analyzing data from wearable sensors, enabling continuous health monitoring and the early detection of health issues.
  • AI in telemedicine triage: AI-driven systems to guide patient triage and improve the quality of remote healthcare services.
  • Health behavior analytics: AI tools for monitoring patient behavior and promoting wellness using digital health apps and interventions.
  1. AI for Healthcare Management and Operations
  • Predictive modeling for hospital resource management: AI algorithms to optimize hospital operations such as patient flow, bed management, and staffing.
  • AI in scheduling and logistics: tools for streamlining appointment scheduling, surgical planning, and supply chain management.
  • Fraud detection in healthcare billing: AI-driven fraud detection systems to prevent billing errors, overcharges, and insurance fraud.
  1. AI and Robotics in Surgery and Treatment Delivery
  • Robotic-assisted surgery: AI advancements in improving precision and outcomes in minimally invasive and robotic surgery.
  • AI for radiation therapy planning: AI systems for optimizing radiation dose delivery and treatment planning in cancer care.
  • Autonomous robotic healthcare systems: the development of fully or semi-autonomous robots for tasks such as drug dispensing, physical therapy, or patient monitoring.
  1. Ethics, Trust, and Fairness in AI for Healthcare
  • AI bias and fairness in healthcare applications: addressing issues related to biased algorithms and ensuring equitable access to AI-driven healthcare services.
  • Data privacy and security: ensuring robust data governance and compliance with privacy regulations such as GDPR and HIPAA in AI healthcare systems.
  • Trustworthiness and transparency: research on building trust with healthcare providers and patients, ensuring AI systems provide transparent, explainable decisions.
  1. Emerging AI Trends in Healthcare
  • Federated learning for healthcare: leveraging decentralized AI models to protect patient privacy while learning from distributed healthcare data.
  • AI in epidemiology and public health: using AI models to predict disease outbreaks, model the spread of infectious diseases, and optimize vaccination strategies.
  • AI and wearable technology integration: combining AI with wearable devices to monitor health in real time and deliver tailored health interventions.

This Special Issue will highlight groundbreaking work in these areas, showcasing how AI is revolutionizing every aspect of healthcare, including the process from diagnostics to treatment, administration, and public health interventions.

Dr. Athanasios Anastasiou
Dr. Antonios N. Gargalionis
Dr. George Dimitrakopoulos
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-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics 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 2400 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 (AI)
  • machine learning (ML)
  • deep learning (DL)
  • biomedicine healthcare technology
  • medical AI
  • health informatics
  • medical imaging AI
  • precision healthcare clinical NLP
  • AI for drug discovery
  • telemedicine AI

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

19 pages, 6054 KB  
Article
A Smart App for the Prevention of Gender-Based Violence Using Artificial Intelligence
by Agostino Giorgio
Electronics 2026, 15(1), 197; https://doi.org/10.3390/electronics15010197 - 1 Jan 2026
Viewed by 462
Abstract
Gender-based violence is a widespread and persistent social scourge. The most effective strategy to reduce its impact is prevention, which has led to the adoption of a hand gesture conventionally recognized as a request for help. In addition, in cases of confirmed risk, [...] Read more.
Gender-based violence is a widespread and persistent social scourge. The most effective strategy to reduce its impact is prevention, which has led to the adoption of a hand gesture conventionally recognized as a request for help. In addition, in cases of confirmed risk, a Judge may order the potential aggressor to wear an electronic bracelet to prevent them from approaching the victim. However, these measures have proven largely insufficient, as incidents of gender-based violence continue to recur. To address this limitation, the author developed an application, named “no pAIn app”, based on artificial intelligence (AI), designed to create a virtual shield for potential victims. The app, which can run on both smartphones and smartwatches, automatically sends help requests with geolocation data when AI detects a real danger situation. The process is fully autonomous and does not require any user intervention, ensuring fast, discreet, and reliable assistance even when the victim cannot act directly. Scenario-based tests in realistic domestic environments showed that configured danger keywords were reliably detected in the vast majority of test cases, with end-to-end alert delivery typically completed within two seconds. Preliminary battery profiling indicated approximately 5% consumption over 24 h of continuous operation confirming the feasibility of long-term daily use. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition)
►▼ Show Figures

Figure 1

46 pages, 6685 KB  
Article
Adversarial Defense for Medical Images
by Min-Jen Tsai, Ya-Chu Lee, Hsin-Ying Lien and Cheng-Chien Liang
Electronics 2025, 14(22), 4384; https://doi.org/10.3390/electronics14224384 - 10 Nov 2025
Viewed by 989
Abstract
The rapid advancement of deep learning is significantly hindered by its vulnerability to adversarial attacks, a critical concern in sensitive domains like medicine where misclassification can have severe, irreversible consequences. This issue directly underscores prediction unreliability and is central to the goals of [...] Read more.
The rapid advancement of deep learning is significantly hindered by its vulnerability to adversarial attacks, a critical concern in sensitive domains like medicine where misclassification can have severe, irreversible consequences. This issue directly underscores prediction unreliability and is central to the goals of Explainable Artificial Intelligence (XAI) and Trustworthy AI. This study addresses this fundamental problem by evaluating the efficacy of denoising techniques against adversarial attacks on medical images. Our primary objective is to assess the performance of various denoising models. The authors generate a test set of adversarial medical images using the one-pixel attack method, which subtly modifies a minimal number of pixels to induce misclassification. The authors propose a novel autoencoder-based denoising model and evaluate it across four diverse medical image datasets: Derma, Pathology, OCT, and Chest. Denoising models were trained by introducing Impulse noise and subsequently tested on the adversarially attacked images, with effectiveness quantitatively evaluated using standard image quality metrics. The results demonstrate that the proposed denoising autoencoder model performs consistently well across all datasets. By mitigating catastrophic failures induced by sparse attacks, this work enhances system dependability and significantly contributes to the development of more robust and reliable deep learning applications for clinical practice. A key limitation is that the evaluation was confined to sparse, pixel-level attacks; robustness to dense, multi-pixel adversarial attacks, such as PGD or AutoAttack, is not guaranteed and requires future investigation. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition)
►▼ Show Figures

Figure 1

14 pages, 6316 KB  
Article
Multimodal Gene Expression and Methylation Profiling Reveals Misclassified Tumors Beyond Histological Diagnosis
by Yasin Mamatjan and Nijiati Abulizi
Electronics 2025, 14(12), 2442; https://doi.org/10.3390/electronics14122442 - 16 Jun 2025
Viewed by 1269
Abstract
Accurate tumor classification is essential for guiding treatment, yet histology alone may overlook key molecular differences or result in misclassification. We present a multimodal strategy that integrates gene expression (mRNA) and DNA methylation data to improve classification accuracy and detect misclassified tumors. Using [...] Read more.
Accurate tumor classification is essential for guiding treatment, yet histology alone may overlook key molecular differences or result in misclassification. We present a multimodal strategy that integrates gene expression (mRNA) and DNA methylation data to improve classification accuracy and detect misclassified tumors. Using 6216 samples from The Cancer Genome Atlas (TCGA), we applied Support Vector Machines (SVMs) and hierarchical clustering to evaluate classification accuracy across single and integrated platforms. mRNA and methylation data alone achieved accuracies of 97% and 95.4%, respectively. Their integration further reduced false positives and improved the identification of outliers, including histologically misclassified cases such as papillary renal cell carcinoma samples clustering with bladder cancer. The integrated approach also revealed molecular subtypes correlated with somatic mutations and patient survival, offering clinically relevant insights. Our findings highlight the value of combining genetic and epigenetic profiles to refine cancer diagnostics. This framework enhances diagnostic precision, supports treatment decisions, and provides a scalable quality control tool for molecular oncology. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition)
►▼ Show Figures

Figure 1

23 pages, 4218 KB  
Article
Integrated Framework for Managing Childhood Obesity Based on Biobanks, AI Tools and Methods, and Serious Games
by Ioannis Vondikakis, Elena Politi, Dimitrios Goulis, George Dimitrakopoulos, Michael Georgoulis, George Saltaouras, Meropi Kontogianni, Theodora Brisimi, Marios Logothetis, Harry Kakoulidis, Marios Prasinos, Athanasios Anastasiou, Ioannis Kakkos, Eleftheria Vellidou, George Matsopoulos and Dimitris Koutsouris
Electronics 2025, 14(10), 2053; https://doi.org/10.3390/electronics14102053 - 19 May 2025
Cited by 3 | Viewed by 1897
Abstract
The growing epidemic of childhood obesity is a major threat to their overall development and poses a number of challenges for health systems. We propose an integrated framework to comprehensively address childhood obesity. The proposed architecture addresses essential data management and pre-processing functionalities [...] Read more.
The growing epidemic of childhood obesity is a major threat to their overall development and poses a number of challenges for health systems. We propose an integrated framework to comprehensively address childhood obesity. The proposed architecture addresses essential data management and pre-processing functionalities to support scalable, secure, and privacy-preserving data processing in distributed environments. We are also incorporating a health data-driven AI approach for predictive analytics and decision support. There is additionally a User Engagement Layer, which serves as the main point of interaction for users. It connects individuals to system capabilities, facilitating data collection, progress monitoring, and insights. Finally, we present four serious games designed to address protective factors (such as physical activity and healthy eating) and mitigate risk factors (such as excessive screen time and unhealthy food choices). The identified educational objectives were translated into game elements including goal setting, social support, and positive reinforcement. In order to facilitate our approach, we have described the essential data flows and user interactions within our Biobank architecture. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition)
►▼ Show Figures

Figure 1

26 pages, 3400 KB  
Article
Deep Audio Features and Self-Supervised Learning for Early Diagnosis of Neonatal Diseases: Sepsis and Respiratory Distress Syndrome Classification from Infant Cry Signals
by Somaye Valizade Shayegh and Chakib Tadj
Electronics 2025, 14(2), 248; https://doi.org/10.3390/electronics14020248 - 9 Jan 2025
Cited by 6 | Viewed by 3426
Abstract
Neonatal mortality remains a critical global challenge, particularly in resource-limited settings with restricted access to advanced diagnostic tools. Early detection of life-threatening conditions like Sepsis and Respiratory Distress Syndrome (RDS), which significantly contribute to neonatal deaths, is crucial for timely interventions and improved [...] Read more.
Neonatal mortality remains a critical global challenge, particularly in resource-limited settings with restricted access to advanced diagnostic tools. Early detection of life-threatening conditions like Sepsis and Respiratory Distress Syndrome (RDS), which significantly contribute to neonatal deaths, is crucial for timely interventions and improved survival rates. This study investigates the use of newborn cry sounds, specifically the expiratory segments (the most informative parts of cry signals) as non-invasive biomarkers for early disease diagnosis. We utilized an expanded and balanced cry dataset, applying Self-Supervised Learning (SSL) models—wav2vec 2.0, WavLM, and HuBERT—to extract feature representations directly from raw cry audio signals. This eliminates the need for manual feature extraction while effectively capturing complex patterns associated with sepsis and RDS. A classifier consisting of a single fully connected layer was placed on top of the SSL models to classify newborns into Healthy, Sepsis, or RDS groups. We fine-tuned the SSL models and classifiers by optimizing hyperparameters using two learning rate strategies: linear and annealing. Results demonstrate that the annealing strategy consistently outperformed the linear strategy, with wav2vec 2.0 achieving the highest accuracy of approximately 90% (89.76%). These findings highlight the potential of integrating this method into Newborn Cry Diagnosis Systems (NCDSs). Such systems could assist medical staff in identifying critically ill newborns, prioritizing care, and improving neonatal outcomes through timely interventions. Full article
(This article belongs to the Special Issue Artificial Intelligence Technologies for Biomedicine and Healthcare Applications, 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop