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Algorithms
Special Issues
Advancing Smart Health and Biomedical Research with Artificial Intelligence Algorithms
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Advancing Smart Health and Biomedical Research with Artificial Intelligence Algorithms

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms and Mathematical Models for Computer-Assisted Diagnostic Systems".

Deadline for manuscript submissions: closed (31 December 2024) | Viewed by 2350

Special Issue Editors

Dr. Jaafar Alghazo

E-Mail Website
Guest Editor
Artificial Intelligence Research Initiative, College of Engineering and Mines, University of North Dakota, Grand Forks, ND 58202-7165, USA
Interests: machine learning; artificial intelligence; image processing; Internet of Things (IoT)
Special Issues, Collections and Topics in MDPI journals
Dr. Ghazanfar Latif

E-Mail Website
Guest Editor
Computer Science Department, Prince Mohammad Bin Fahd University, Khobar 34754, Saudi Arabia
Interests: artificial intelligence; machine learning; image processing; Internet of Things; robotics
Dr. Elena Tanfani

E-Mail Website
Guest Editor
Department of Economics and Business Studies, University of Genoa, Via Vivaldi 5, 16126 Genova, Italy
Interests: health monitoring; healthcare; operations research

Special Issue Information

Dear Colleagues,

The medical field is experiencing notable progress by integrating advanced technologies designed to provide optimal solutions for supporting medical professionals in smart health and biomedical research. As an example, radiology has observed the most applications of patented and FDA-approved artificial intelligence (AI)-based devices. This Special Issue encourages researchers to extend the potential of AI to diverse medical disciplines. Whether for medical diagnosis or drug development, the recent advances in technologies such as artificial intelligence (AI), machine learning (ML), deep learning (DL), and data sciences can advance research across the spectrum of smart health and biomedical research. This Special Issue aims to inspire researchers to explore the development of new AI algorithms or enhance existing AI-based algorithms in smart health and biomedical research. The research articles contributed to this issue are anticipated to lay the groundwork for developing patented devices with promising applications in the medical field.

Recent breakthroughs in machine learning (ML), artificial intelligence (AI), deep learning, and high performance coupled with the availability of new datasets have made advancements in smart health and biomedical research possible. Documented in the Summary of the Big Data and High-End Computing Interagency Working Groups Joint Workshop on the Convergence of High-Performance Computing, Big Data, and Machine Learning, these developments pave the way for unprecedented progress in medical research. Development or enhancement of algorithms concurrently address various aspects of human biology, physiology, and behavior, thereby enabling the creation of personalized, predictive models.

Dr. Jaafar Alghazo
Dr. Ghazanfar Latif
Dr. Elena Tanfani
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. Algorithms 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

  • AI algorithms in healthcare
  • AI algorithms in biomedical research
  • medical diagnostics
  • smart healthcare
  • ML and big data
  • sensor technologies
  • privacy in healthcare
  • AI-enabled medical imaging

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Published Papers (3 papers)

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Research

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18 pages, 4019 KiB  
Article
Seizure Detection in Medical IoT: Hybrid CNN-LSTM-GRU Model with Data Balancing and XAI Integration
by Hanaa Torkey, Sonia Hashish, Samia Souissi, Ezz El-Din Hemdan and Amged Sayed
Algorithms 2025, 18(2), 77; https://doi.org/10.3390/a18020077 (registering DOI) - 1 Feb 2025
Viewed by 281
Abstract
The brain acts as the body’s central command, overseeing diverse functions including thought, memory, speech, movement, and the regulation of various organs. When healthy, the brain functions seamlessly and automatically; however, disruptions can lead to serious conditions such as Alzheimer’s Disease, Brain Cancer, [...] Read more.
The brain acts as the body’s central command, overseeing diverse functions including thought, memory, speech, movement, and the regulation of various organs. When healthy, the brain functions seamlessly and automatically; however, disruptions can lead to serious conditions such as Alzheimer’s Disease, Brain Cancer, Stroke, and Epilepsy. Epilepsy, a neurological disorder marked by recurrent seizures, results from irregular electrical activity in the brain. These seizures, which can strain both patients and neurologists, are characterized by symptoms like the loss of awareness, unusual behavior, and confusion. This study presents an efficient EEG-based epileptic seizure detection framework utilizing a hybrid Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) models approach to support automated and accurate diagnosis. Handling imbalanced EEG data, which can otherwise bias model outcomes and reduce predictive accuracy, is a key focus. Experimental results indicate that the proposed framework generally outperforms other Deep Learning and Machine Learning techniques with the highest accuracy at 99.13%. Likewise, an Explainable Artificial Intelligence (XAI) called SHAP (SHapley Additive exPlanations) is utilized to analyze the results and to improve the interpretability of the models from medical decision-making. This framework aligns with the objectives of the Medical Internet of Things (MIoT), advancing smart medical applications and services for effective epileptic seizure detection. Full article
(This article belongs to the Special Issue Advancing Smart Health and Biomedical Research with Artificial Intelligence Algorithms)
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Review

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16 pages, 1128 KiB  
Review
Artificial Intelligence and Algorithmic Approaches of Health Security Systems: A Review
by Savina Mariettou, Constantinos Koutsojannis and Vassilios Triantafillou
Algorithms 2025, 18(2), 59; https://doi.org/10.3390/a18020059 - 22 Jan 2025
Viewed by 541
Abstract
This paper explores the overall picture regarding healthcare security systems through an extensive literature review. As the healthcare sector has now become digitalized, the security of healthcare systems and, by extension, the protection of patient data is a key concern in the modern [...] Read more.
This paper explores the overall picture regarding healthcare security systems through an extensive literature review. As the healthcare sector has now become digitalized, the security of healthcare systems and, by extension, the protection of patient data is a key concern in the modern era of technological advances. Therefore, a secure and integrated system is now essential. Thus, to evaluate the relationship between security systems and healthcare quality, we conducted literature research to identify studies reporting their association. The timeline of our review is based on published studies covering the period from 2018 to 2024, with entries identified through a search of the relevant literature, focusing on the most recent developments due to advances in artificial intelligence and algorithmic approaches. Thirty-two studies were included in our final survey. Our findings underscore the critical role of security systems in healthcare that significantly improve patient outcomes and maintain the integrity of healthcare services. According to our approach, the studies analyzed highlight the growing importance of advanced security frameworks, especially those incorporating artificial intelligence and algorithmic methodologies, in safeguarding healthcare systems while enhancing patient care quality. According to this study, most of the research analyzed uses algorithmic technology approaches, many researchers prove that ransomware is the most common threat to hospital information systems, and more studies are needed to evaluate the performance of the systems created against this kind of attack. Full article
(This article belongs to the Special Issue Advancing Smart Health and Biomedical Research with Artificial Intelligence Algorithms)
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Other

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27 pages, 553 KiB  
Systematic Review
Integrating Artificial Intelligence, Internet of Things, and Sensor-Based Technologies: A Systematic Review of Methodologies in Autism Spectrum Disorder Detection
by Georgios Bouchouras and Konstantinos Kotis
Algorithms 2025, 18(1), 34; https://doi.org/10.3390/a18010034 - 9 Jan 2025
Viewed by 955
Abstract
This paper presents a systematic review of the emerging applications of artificial intelligence (AI), Internet of Things (IoT), and sensor-based technologies in the diagnosis of autism spectrum disorder (ASD). The integration of these technologies has led to promising advances in identifying unique behavioral, [...] Read more.
This paper presents a systematic review of the emerging applications of artificial intelligence (AI), Internet of Things (IoT), and sensor-based technologies in the diagnosis of autism spectrum disorder (ASD). The integration of these technologies has led to promising advances in identifying unique behavioral, physiological, and neuroanatomical markers associated with ASD. Through an examination of recent studies, we explore how technologies such as wearable sensors, eye-tracking systems, virtual reality environments, neuroimaging, and microbiome analysis contribute to a holistic approach to ASD diagnostics. The analysis reveals how these technologies facilitate non-invasive, real-time assessments across diverse settings, enhancing both diagnostic accuracy and accessibility. The findings underscore the transformative potential of AI, IoT, and sensor-based driven tools in providing personalized and continuous ASD detection, advocating for data-driven approaches that extend beyond traditional methodologies. Ultimately, this review emphasizes the role of technology in improving ASD diagnostic processes, paving the way for targeted and individualized assessments. Full article
(This article belongs to the Special Issue Advancing Smart Health and Biomedical Research with Artificial Intelligence Algorithms)
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