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Search Results (1,525)

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Keywords = artificial intelligence in healthcare

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32 pages, 858 KiB  
Review
Designing Sustainable and Acoustically Optimized Dental Spaces: A Comprehensive Review of Soundscapes in Dental Office Environments
by Maria Antoniadou, Eleni Ioanna Tzaferi and Christina Antoniadou
Appl. Sci. 2025, 15(15), 8167; https://doi.org/10.3390/app15158167 - 23 Jul 2025
Abstract
The acoustic environment of dental clinics plays a critical role in shaping patient experience, staff performance, and overall clinical effectiveness. This comprehensive review, supported by systematic search procedures, investigates how soundscapes in dental settings influence psychological, physiological, and operational outcomes. A total of [...] Read more.
The acoustic environment of dental clinics plays a critical role in shaping patient experience, staff performance, and overall clinical effectiveness. This comprehensive review, supported by systematic search procedures, investigates how soundscapes in dental settings influence psychological, physiological, and operational outcomes. A total of 60 peer-reviewed studies were analyzed across dental, healthcare, architectural, and environmental psychology disciplines. Findings indicate that mechanical noise from dental instruments, ambient reverberation, and inadequate acoustic zoning contribute significantly to patient anxiety and professional fatigue. The review identifies emerging strategies for acoustic optimization, including biophilic and sustainable design principles, sound-masking systems, and adaptive sound environments informed by artificial intelligence. Special attention is given to the integration of lean management and circular economy practices for sustainable dental architecture. A design checklist and practical framework are proposed for use by dental professionals, architects, and healthcare planners. Although limited by the predominance of observational studies and geographic bias in the existing literature, this review offers a comprehensive, interdisciplinary synthesis. It highlights the need for future clinical trials, real-time acoustic assessments, and participatory co-design methods to enhance acoustic quality in dental settings. Overall, the study positions sound design as a foundational element in creating patient-centered, ecologically responsible dental environments. Full article
(This article belongs to the Special Issue Soundscapes in Architecture and Urban Planning)
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34 pages, 1835 KiB  
Article
Advancing Neurodegenerative Disease Management: Technical, Ethical, and Regulatory Insights from the NeuroPredict Platform
by Marilena Ianculescu, Lidia Băjenaru, Ana-Mihaela Vasilevschi, Maria Gheorghe-Moisii and Cristina-Gabriela Gheorghe
Future Internet 2025, 17(7), 320; https://doi.org/10.3390/fi17070320 - 21 Jul 2025
Viewed by 105
Abstract
On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, [...] Read more.
On a worldwide scale, neurodegenerative diseases, including multiple sclerosis, Parkinson’s, and Alzheimer’s, face considerable healthcare challenges demanding the development of novel approaches to early detection and efficient treatment. With its ability to provide real-time patient monitoring, customized medical care, and advanced predictive analytics, artificial intelligence (AI) is fundamentally transforming the way healthcare is provided. Through the integration of wearable physiological sensors, motion sensors, and neurological assessment tools, the NeuroPredict platform harnesses AI and smart sensor technologies to enhance the management of specific neurodegenerative diseases. Machine learning algorithms process these data flows to find patterns that point out disease evolution. This paper covers the design and architecture of the NeuroPredict platform, stressing the ethical and regulatory requirements that guide its development. Initial development of AI algorithms for disease monitoring, technical achievements, and constant enhancements driven by early user feedback are addressed in the discussion section. To ascertain the platform’s trustworthiness and data security, it also points towards risk analysis and mitigation approaches. The NeuroPredict platform’s capability for achieving AI-driven smart healthcare solutions is highlighted, even though it is currently in the development stage. Subsequent research is expected to focus on boosting data integration, expanding AI models, and providing regulatory compliance for clinical application. The current results are based on incremental laboratory tests using simulated user roles, with no clinical patient data involved so far. This study reports an experimental technology evaluation of modular components of the NeuroPredict platform, integrating multimodal sensors and machine learning pipelines in a laboratory-based setting, with future co-design and clinical validation foreseen for a later project phase. Full article
(This article belongs to the Special Issue Artificial Intelligence-Enabled Smart Healthcare)
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22 pages, 3075 KiB  
Review
An Innovative Approach to Medical Education: Leveraging Generative Artificial Intelligence to Promote Inclusion and Support for Indigenous Students
by Isaac Oluwatobi Akefe, Victoria Aderonke Adegoke, Elijah Akefe, Daniel Schweitzer and Stephen Bolaji
Trends High. Educ. 2025, 4(3), 36; https://doi.org/10.3390/higheredu4030036 - 21 Jul 2025
Viewed by 92
Abstract
Indigenous students remain significantly underrepresented in medical education, contributing to persistent health inequities in their communities. Systemic barriers, including cultural isolation, inadequate resources, and biased curricula, hinder their success. But what if generative artificial intelligence (GAI) could be the game-changer? This scoping review [...] Read more.
Indigenous students remain significantly underrepresented in medical education, contributing to persistent health inequities in their communities. Systemic barriers, including cultural isolation, inadequate resources, and biased curricula, hinder their success. But what if generative artificial intelligence (GAI) could be the game-changer? This scoping review explores the potential of generative artificial intelligence (GAI) in making medical education more inclusive and supportive for Indigenous students through a comprehensive analysis of existing literature. From AI-powered engagement platforms to personalised learning systems and immersive simulations, GAI can be harnessed to bridge the gap. While GAI holds promise, challenges like biased datasets and limited access to technology must be addressed. To unlock GAI’s potential, we recommend faculty development, expansion of digital infrastructure, and Indigenous-led AI design. By carefully harnessing GAI, medical schools can take a crucial step towards creating a more diverse and equitable healthcare workforce, ultimately improving health outcomes for Indigenous communities. Full article
(This article belongs to the Special Issue Redefining Academia: Innovative Approaches to Diversity and Inclusion)
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25 pages, 4050 KiB  
Review
Network Pharmacology-Driven Sustainability: AI and Multi-Omics Synergy for Drug Discovery in Traditional Chinese Medicine
by Lifang Yang, Hanye Wang, Zhiyao Zhu, Ye Yang, Yin Xiong, Xiuming Cui and Yuan Liu
Pharmaceuticals 2025, 18(7), 1074; https://doi.org/10.3390/ph18071074 - 21 Jul 2025
Viewed by 117
Abstract
Traditional Chinese medicine (TCM), a holistic medical system rooted in dialectical theories and natural product-based therapies, has served as a cornerstone of healthcare systems for millennia. While its empirical efficacy is widely recognized, the polypharmacological mechanisms stemming from its multi-component nature remain poorly [...] Read more.
Traditional Chinese medicine (TCM), a holistic medical system rooted in dialectical theories and natural product-based therapies, has served as a cornerstone of healthcare systems for millennia. While its empirical efficacy is widely recognized, the polypharmacological mechanisms stemming from its multi-component nature remain poorly characterized. The conventional trial-and-error approaches for bioactive compound screening from herbs raise sustainability concerns, including excessive resource consumption and suboptimal temporal efficiency. The integration of artificial intelligence (AI) and multi-omics technologies with network pharmacology (NP) has emerged as a transformative methodology aligned with TCM’s inherent “multi-component, multi-target, multi-pathway” therapeutic characteristics. This convergent review provides a computational framework to decode complex bioactive compound–target–pathway networks through two synergistic strategies, (i) NP-driven dynamics interaction network modeling and (ii) AI-enhanced multi-omics data mining, thereby accelerating drug discovery and reducing experimental costs. Our analysis of 7288 publications systematically maps NP-AI–omics integration workflows for natural product screening. The proposed framework enables sustainable drug discovery through data-driven compound prioritization, systematic repurposing of herbal formulations via mechanism-based validation, and the development of evidence-based novel TCM prescriptions. This paradigm bridges empirical TCM knowledge with mechanism-driven precision medicine, offering a theoretical basis for reconciling traditional medicine with modern pharmaceutical innovation. Full article
(This article belongs to the Special Issue Sustainable Approaches and Strategies for Bioactive Natural Compounds)
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25 pages, 5160 KiB  
Review
A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare
by Silvia L. Chaparro-Cárdenas, Julian-Andres Ramirez-Bautista, Juan Terven, Diana-Margarita Córdova-Esparza, Julio-Alejandro Romero-Gonzalez, Alfonso Ramírez-Pedraza and Edgar A. Chavez-Urbiola
Healthcare 2025, 13(14), 1763; https://doi.org/10.3390/healthcare13141763 - 21 Jul 2025
Viewed by 236
Abstract
Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of [...] Read more.
Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing direct risks to patients. However, their integration into healthcare requires careful consideration of ethical, regulatory, and safety constraints in light of the sensitivity and nonlinear nature of human data. In this review, we examine recent progress in DTs over the past seven years and explore broader trends in AI-augmented DTs, focusing particularly on movement rehabilitation. Our goal is to provide a comprehensive understanding of how DTs bolstered by AI can transform healthcare delivery, medical research, and personalized care. We discuss implementation challenges such as data privacy, clinical validation, and scalability along with opportunities for more efficient, safe, and patient-centered healthcare systems. By addressing these issues, this review highlights key insights and directions for future research to guide the proactive and ethical adoption of DTs in healthcare. Full article
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13 pages, 281 KiB  
Review
Genetics and Clinical Findings Associated with Early-Onset Myopia and Retinal Detachment in Saudi Arabia
by Mariam M. AlEissa, Abrar A. Alhawsawi, Doaa Milibari, Patrik Schatz, Hani B. AlBalawi, Naif M. Alali, Khaled K. Abu-Amero, Syed Hameed and Moustafa S. Magliyah
Genes 2025, 16(7), 848; https://doi.org/10.3390/genes16070848 - 21 Jul 2025
Viewed by 248
Abstract
Autosomal recessive types of both syndromic and non-syndromic inherited myopia are common in Saudi Arabia (SA) because many people marry their relatives. The prevalence of syndromic myopathies in SA, like Stickler syndrome (SS), Knobloch syndrome (KS), and Marfan syndrome (MFS), further complicates the [...] Read more.
Autosomal recessive types of both syndromic and non-syndromic inherited myopia are common in Saudi Arabia (SA) because many people marry their relatives. The prevalence of syndromic myopathies in SA, like Stickler syndrome (SS), Knobloch syndrome (KS), and Marfan syndrome (MFS), further complicates the disease spectrum. The causative genes linked to the Knobloch, Marfan, and Pierson syndromes are COL18A1, FBN1, and LAMB2, respectively. Additionally, we found recessive types of non-syndromic high myopia that have a high chance of causing retinal detachment, like those linked to LRPAP1 and LEPREL1. In these cases, regular evaluation and early intervention, including prophylactic laser photocoagulation and pars plana vitrectomy, may improve the outcome. Advancements in genetic testing for diagnosis and prevention accelerate detection, facilitate early interventions, and provide genetic counseling. The utilization of artificial intelligence (AI), machine learning (ML), and the advancement of gene therapy offer promising avenues for personalized care. We place a high value on using genetic knowledge to create a national screening program and patient registry aimed at understanding the national burden of myopia, knowing that we have a high rate of consanguinity, which reflects pathogenic homozygous alleles and founder mutations. This initiative will incorporate genetic counseling and leverage innovative technologies, which are crucial for disease management, early identification, and prevention in Saudi Arabia’s healthcare system. Full article
(This article belongs to the Section Human Genomics and Genetic Diseases)
15 pages, 508 KiB  
Review
The Role of Artificial Intelligence in the Diagnosis and Management of Diabetic Retinopathy
by Areeb Ansari, Nabiha Ansari, Usman Khalid, Daniel Markov, Kristian Bechev, Vladimir Aleksiev, Galabin Markov and Elena Poryazova
J. Clin. Med. 2025, 14(14), 5150; https://doi.org/10.3390/jcm14145150 - 20 Jul 2025
Viewed by 275
Abstract
Background/Objectives: Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes mellitus and a leading cause of vision impairment worldwide. Early detection and timely management are critical in preventing vision loss, yet current screening programs face challenges, including limited specialist availability and [...] Read more.
Background/Objectives: Diabetic retinopathy (DR) is a progressive microvascular complication of diabetes mellitus and a leading cause of vision impairment worldwide. Early detection and timely management are critical in preventing vision loss, yet current screening programs face challenges, including limited specialist availability and variability in diagnoses, particularly in underserved areas. This literature review explores the evolving role of artificial intelligence (AI) in enhancing the diagnosis, screening, and management of diabetic retinopathy. It examines AI’s potential to improve diagnostic accuracy, accessibility, and patient outcomes through advanced machine-learning and deep-learning algorithms. Methods: We conducted a non-systematic review of the published literature to explore advancements in the diagnostics of diabetic retinopathy. Relevant articles were identified by searching the PubMed and Google Scholar databases. Studies focusing on the application of artificial intelligence in screening, diagnosis, and improving healthcare accessibility for diabetic retinopathy were included. Key information was extracted and synthesized to provide an overview of recent progress and clinical implications. Conclusions: Artificial intelligence holds transformative potential in diabetic retinopathy care by enabling earlier detection, improving screening coverage, and supporting individualized disease management. Continued research and ethical deployment will be essential to maximize AI’s benefits and address challenges in real-world applications, ultimately improving global vision health outcomes. Full article
(This article belongs to the Section Ophthalmology)
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27 pages, 1686 KiB  
Systematic Review
A Systematic Review of Artificial Intelligence (AI) and Machine Learning (ML) in Pharmaceutical Supply Chain (PSC) Resilience: Current Trends and Future Directions
by Shireen Al-Hourani and Dua Weraikat
Sustainability 2025, 17(14), 6591; https://doi.org/10.3390/su17146591 - 19 Jul 2025
Viewed by 313
Abstract
The resilience of the pharmaceutical supply chain (PSC) is crucial to ensuring the availability of medical products. However, increasing complexity and logistical bottlenecks have exposed weaknesses within PSC frameworks. These challenges underscore the urgent need for more resilient and intelligent supply chain solutions. [...] Read more.
The resilience of the pharmaceutical supply chain (PSC) is crucial to ensuring the availability of medical products. However, increasing complexity and logistical bottlenecks have exposed weaknesses within PSC frameworks. These challenges underscore the urgent need for more resilient and intelligent supply chain solutions. Recently, Artificial Intelligence and machine learning (AI/ML) have emerged as transformative technologies to enhance PSC resilience. This study presents a systematic review evaluating the role of AI/ML in advancing PSC resilience and their applications across PSC functions. A comprehensive search of five academic databases (Scopus, the Web of Science, IEEE Xplore, PubMed, and EMBASE) identified 89 peer-reviewed studies published between 2019 and 2025. PRISMA 2020 guidelines were implemented, resulting in a final dataset of 32 studies. In addition to analyzing applications, this study identifies the AI/ML grouped into five main categories, providing a clearer understanding of their impact on PSC resilience. The findings reveal that despite AI/ML’s promise, significant research gaps persist. Particularly, AI/ML-driven regulatory compliance and real-time supplier collaboration remain underexplored. Over 59.3% of studies fail to address regulatory frameworks and ethical considerations. In addition, major challenges emerge such as the limited real-world deployment of AI/ML-driven solutions and the lack of managerial impacts on PSC resilience. This study emphasizes the need for stronger regulatory frameworks, broader empirical validation, and AI/ML-driven predictive modeling. This study proposes recommendations for future research to foster more efficient, transparent and ethical PSCs capable of navigating the complexities of global healthcare. Full article
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21 pages, 1420 KiB  
Article
Disaster Preparedness in Saudi Arabia’s Primary Healthcare Workers for Human Well-Being and Sustainability
by Mona Raif Alrowili, Alia Mohammed Almoajel, Fahad Magbol Alneam and Riyadh A. Alhazmi
Sustainability 2025, 17(14), 6562; https://doi.org/10.3390/su17146562 - 18 Jul 2025
Viewed by 262
Abstract
The preparedness of healthcare workers for disaster situations depends on their technical skills, disaster knowledge, and psychosocial strength, including teamwork and emotional regulation. This study aims to assess disaster preparedness among healthcare professionals in primary healthcare centers (PHCs) in Alqurayat, Saudi Arabia, with [...] Read more.
The preparedness of healthcare workers for disaster situations depends on their technical skills, disaster knowledge, and psychosocial strength, including teamwork and emotional regulation. This study aims to assess disaster preparedness among healthcare professionals in primary healthcare centers (PHCs) in Alqurayat, Saudi Arabia, with a specific focus on evaluating technical competencies, psychosocial readiness, and predictive modeling of preparedness levels. A mixed-methods approach was employed, incorporating structured questionnaires, semi-structured interviews, and observational data from disaster drills to evaluate the preparedness levels of 400 healthcare workers, including doctors, nurses, and administrative staff. The results showed that while knowledge (mean: 3.9) and skills (mean: 4.0) were generally moderate to high, notable gaps in overall preparedness remained. Importantly, 69.5% of participants reported enhanced readiness following simulation drills. Machine learning models, including Random Forest and Artificial Neural Networks, were used to predict preparedness outcomes based on psychosocial variables such as emotional intelligence, teamwork, and stress management. Sentiment analysis and topic modeling of qualitative responses revealed key themes including communication barriers, psychological safety, and the need for ongoing training. The findings highlight the importance of integrating both technical competencies and psychosocial resilience into disaster management programs. This study contributes an innovative framework for evaluating preparedness and offers practical insights for policymakers, disaster planners, and health training institutions aiming to strengthen the sustainability and responsiveness of primary healthcare systems. Full article
(This article belongs to the Special Issue Occupational Mental Health)
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40 pages, 4319 KiB  
Review
Biophilic Design in the Built Environment: Trends, Gaps and Future Directions
by Bekir Hüseyin Tekin, Gizem Izmir Tunahan, Zehra Nur Disci and Hatice Sule Ozer
Buildings 2025, 15(14), 2516; https://doi.org/10.3390/buildings15142516 - 17 Jul 2025
Viewed by 253
Abstract
Biophilic design has emerged as a multidimensional response to growing concerns about health, well-being, and ecological balance in the built environment. Despite its rising prominence, research on the topic remains fragmented across building typologies, user groups, and geographic contexts. This study presents a [...] Read more.
Biophilic design has emerged as a multidimensional response to growing concerns about health, well-being, and ecological balance in the built environment. Despite its rising prominence, research on the topic remains fragmented across building typologies, user groups, and geographic contexts. This study presents a comprehensive review of the biophilic design literature, employing a hybrid methodology combining structured content analysis and bibliometric mapping. All peer-reviewed studies indexed in the Web of Science and Scopus were manually screened for architectural relevance and systematically coded. A total of 435 studies were analysed to identify key trends, thematic patterns, and research gaps in the biophilic design discipline. This review categorises the literature by methodological strategies, building typologies, spatial scales, population groups, and specific biophilic design parameters. It also examines geographic and cultural dimensions, including climate responsiveness, heritage buildings, policy frameworks, theory development, pedagogy, and COVID-19-related research. The findings show a strong emphasis on institutional contexts, particularly workplaces, schools, and healthcare, and a reliance on perception-based methods such as surveys and experiments. In contrast, advanced tools like artificial intelligence, simulation, and VR are notably underused. Few studies engage with neuroarchitecture or neuroscience-informed approaches, despite growing recognition of how spatial design can influence cognitive and emotional responses. Experimental and biometric methods remain scarce among the few relevant contributions, revealing a missed opportunity to connect biophilic strategies with empirical evidence. Regarding biophilic parameters, greenery, daylight, and sensory experience are the most studied parameters, while psychological parameters remain underexplored. Cultural and climate-specific considerations appear in relatively few studies, and many fail to define a user group or building typology. This review highlights the need for more inclusive, context-responsive, and methodologically diverse research. By bridging macro-scale bibliometric patterns with fine-grained thematic insights, this study provides a replicable review model and valuable reference for advancing biophilic design as an evidence-based, adaptable, and human-centred approach to sustainable architecture. Full article
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13 pages, 1243 KiB  
Review
Evidence-Based Medicine: Past, Present, Future
by Filippos Triposkiadis and Dirk L. Brutsaert
J. Clin. Med. 2025, 14(14), 5094; https://doi.org/10.3390/jcm14145094 - 17 Jul 2025
Viewed by 206
Abstract
Early medical traditions include those of ancient Babylonia, China, Egypt, and India. The roots of modern Western medicine, however, go back to ancient Greece. During the Renaissance, physicians increasingly relied on observation and experimentation to understand the human body and develop new techniques [...] Read more.
Early medical traditions include those of ancient Babylonia, China, Egypt, and India. The roots of modern Western medicine, however, go back to ancient Greece. During the Renaissance, physicians increasingly relied on observation and experimentation to understand the human body and develop new techniques for diagnosis and treatment. The discovery of antibiotics, antiseptics, and other drugs in the 19th century accelerated the development of modern medicine, the latter being fueled further by advances in technology, research, a better understanding of the human body, and, most recently, the introduction of evidence-based medicine (EBM). The EBM model de-emphasized intuition, unsystematic clinical experience, and pathophysiologic rationale as sufficient grounds for clinical decision-making and stressed the examination of evidence from clinical research. A later EBM model additionally incorporated clinical expertise and the latest model of EBM patients’ preferences and actions. In this review article, we argue that in the era of precision medicine, major EBM principles must be based on (a) the systematic identification, analysis, and utility of big data using artificial intelligence; (b) the magnifying effect of medical interventions by means of the physician–patient interaction, the latter being guided by the physician’s expertise, intuition, and philosophical beliefs; and (c) the patient preferences, since, in healthcare under precision medicine, the patient will be a central stakeholder contributing data and actively participating in shared decision-making. Full article
(This article belongs to the Section Clinical Research Methods)
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24 pages, 2173 KiB  
Article
A Novel Ensemble of Deep Learning Approach for Cybersecurity Intrusion Detection with Explainable Artificial Intelligence
by Abdullah Alabdulatif
Appl. Sci. 2025, 15(14), 7984; https://doi.org/10.3390/app15147984 - 17 Jul 2025
Viewed by 301
Abstract
In today’s increasingly interconnected digital world, cyber threats have grown in frequency and sophistication, making intrusion detection systems a critical component of modern cybersecurity frameworks. Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and [...] Read more.
In today’s increasingly interconnected digital world, cyber threats have grown in frequency and sophistication, making intrusion detection systems a critical component of modern cybersecurity frameworks. Traditional IDS methods, often based on static signatures and rule-based systems, are no longer sufficient to detect and respond to complex and evolving attacks. To address these challenges, Artificial Intelligence and machine learning have emerged as powerful tools for enhancing the accuracy, adaptability, and automation of IDS solutions. This study presents a novel, hybrid ensemble learning-based intrusion detection framework that integrates deep learning and traditional ML algorithms with explainable artificial intelligence for real-time cybersecurity applications. The proposed model combines an Artificial Neural Network and Support Vector Machine as base classifiers and employs a Random Forest as a meta-classifier to fuse predictions, improving detection performance. Recursive Feature Elimination is utilized for optimal feature selection, while SHapley Additive exPlanations (SHAP) provide both global and local interpretability of the model’s decisions. The framework is deployed using a Flask-based web interface in the Amazon Elastic Compute Cloud environment, capturing live network traffic and offering sub-second inference with visual alerts. Experimental evaluations using the NSL-KDD dataset demonstrate that the ensemble model outperforms individual classifiers, achieving a high accuracy of 99.40%, along with excellent precision, recall, and F1-score metrics. This research not only enhances detection capabilities but also bridges the trust gap in AI-powered security systems through transparency. The solution shows strong potential for application in critical domains such as finance, healthcare, industrial IoT, and government networks, where real-time and interpretable threat detection is vital. Full article
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15 pages, 807 KiB  
Viewpoint
The New Horizon: A Viewpoint of Novel Drugs, Biomarkers, Artificial Intelligence, and Self-Management in Improving Kidney Transplant Outcomes
by Artur Quintiliano and Andrew J. Bentall
J. Clin. Med. 2025, 14(14), 5077; https://doi.org/10.3390/jcm14145077 - 17 Jul 2025
Viewed by 200
Abstract
The increasing prevalence of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) has led to a growing demand for kidney transplantation (KTx). Identifying risk factors that enable improved allograft survival through novel therapeutic agents, advanced biomarkers, and artificial intelligence (AI)-driven data integration [...] Read more.
The increasing prevalence of chronic kidney disease (CKD) and end-stage kidney disease (ESKD) has led to a growing demand for kidney transplantation (KTx). Identifying risk factors that enable improved allograft survival through novel therapeutic agents, advanced biomarkers, and artificial intelligence (AI)-driven data integration are critical to addressing this challenge. Drugs, such as SGLT2 inhibitors and finerenone, have demonstrated improved outcomes in patients but lack comprehensive long-term evidence in KTx patients. The use of biomarkers, including circulating cytokines and transcriptomics, coupled with AI, could enhance early detection and personalized treatment strategies. Addressing patient self-management and addressing health access disparities may be more achievable using technologies used at home rather than traditional models of healthcare and thus lead to increased transplant success, both in terms of transplantation rates and allograft longevity. Full article
(This article belongs to the Special Issue Kidney Transplantation: State of the Art Knowledge)
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27 pages, 49290 KiB  
Review
AI-Driven Robotics: Innovations in Design, Perception, and Decision-Making
by Lei Li, Li Li, Mantian Li and Ke Liang
Machines 2025, 13(7), 615; https://doi.org/10.3390/machines13070615 - 17 Jul 2025
Viewed by 326
Abstract
Robots are increasingly being used across industries, healthcare, and service sectors to perform a wide range of tasks. However, as these tasks become more complex and environments more unpredictable, the need for adaptable robots continues to grow—bringing with it greater technological challenges. Artificial [...] Read more.
Robots are increasingly being used across industries, healthcare, and service sectors to perform a wide range of tasks. However, as these tasks become more complex and environments more unpredictable, the need for adaptable robots continues to grow—bringing with it greater technological challenges. Artificial intelligence (AI), driven by large datasets and advanced algorithms, plays a pivotal role in addressing these challenges and advancing robotics. AI enhances robot design by making it more intelligent and flexible, significantly improving robot perception to better understand and respond to surrounding environments and empowering more intelligent control and decision-making. In summary, AI contributes to robotics through design optimization, environmental perception, and intelligent decision-making. This article explores the driving role of AI in robotics and presents detailed examples of its integration with fields such as embodied intelligence, humanoid robots, big data, and large AI models, while also discussing future prospects and challenges in this rapidly evolving field. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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40 pages, 17591 KiB  
Article
Research and Education in Robotics: A Comprehensive Review, Trends, Challenges, and Future Directions
by Mutaz Ryalat, Natheer Almtireen, Ghaith Al-refai, Hisham Elmoaqet and Nathir Rawashdeh
J. Sens. Actuator Netw. 2025, 14(4), 76; https://doi.org/10.3390/jsan14040076 - 16 Jul 2025
Viewed by 524
Abstract
Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution [...] Read more.
Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers. Full article
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