Search Results (295)

Background: Artificial intelligence (AI) and machine learning (ML) have been reshaping maternal, fetal, neonatal, and reproductive healthcare by enhancing risk prediction, diagnostic accuracy, and operational efficiency across the perinatal continuum. However, no comprehensive synthesis has yet been published. Objective: To conduct a scoping review of reviews of AI/ML applications spanning reproductive, prenatal, postpartum, neonatal, and early child-development care. Methods: We searched PubMed, Embase, the Cochrane Library, Web of Science, and Scopus through April 2025. Two reviewers independently screened records, extracted data, and assessed methodological quality using AMSTAR 2 for systematic reviews, ROBIS for bias assessment, SANRA for narrative reviews, and JBI guidance for scoping reviews. Results: Thirty-nine reviews met our inclusion criteria. In preconception and fertility treatment, convolutional neural network-based platforms can identify viable embryos and key sperm parameters with over 90 percent accuracy, and machine-learning models can personalize follicle-stimulating hormone regimens to boost mature oocyte yield while reducing overall medication use. Digital sexual-health chatbots have enhanced patient education, pre-exposure prophylaxis adherence, and safer sexual behaviors, although data-privacy safeguards and bias mitigation remain priorities. During pregnancy, advanced deep-learning models can segment fetal anatomy on ultrasound images with more than 90 percent overlap compared to expert annotations and can detect anomalies with sensitivity exceeding 93 percent. Predictive biometric tools can estimate gestational age within one week with accuracy and fetal weight within approximately 190 g. In the postpartum period, AI-driven decision-support systems and conversational agents can facilitate early screening for depression and can guide follow-up care. Wearable sensors enable remote monitoring of maternal blood pressure and heart rate to support timely clinical intervention. Within neonatal care, the Heart Rate Observation (HeRO) system has reduced mortality among very low-birth-weight infants by roughly 20 percent, and additional AI models can predict neonatal sepsis, retinopathy of prematurity, and necrotizing enterocolitis with area-under-the-curve values above 0.80. From an operational standpoint, automated ultrasound workflows deliver biometric measurements at about 14 milliseconds per frame, and dynamic scheduling in IVF laboratories lowers staff workload and per-cycle costs. Home-monitoring platforms for pregnant women are associated with 7–11 percent reductions in maternal mortality and preeclampsia incidence. Despite these advances, most evidence derives from retrospective, single-center studies with limited external validation. Low-resource settings, especially in Sub-Saharan Africa, remain under-represented, and few AI solutions are fully embedded in electronic health records. Conclusions: AI holds transformative promise for perinatal care but will require prospective multicenter validation, equity-centered design, robust governance, transparent fairness audits, and seamless electronic health record integration to translate these innovations into routine practice and improve maternal and neonatal outcomes. Full article

Surgery remains a healthcare intervention with significant risks for patients. Novel technologies can now enhance the peri-operative workflow, with artificial intelligence (AI) and extended reality (XR) to assist with pre-operative planning. This review focuses on innovation in AI, XR and imaging for hepato-biliary surgery planning. The clinical challenges in hepato-biliary surgery arise from heterogeneity of clinical presentations, the need for multiple imaging modalities and highly variable local anatomy. AI-based models have been developed for risk prediction and multi-disciplinary tumor (MDT) board meetings. The future could involve an on-demand and highly accurate AI-powered decision tool for hepato-biliary surgery, assisting the surgeon to make the most informed decision on the treatment plan, conferring the best possible outcome for individual patients. Advances in AI can also be used to automate image interpretation and 3D modelling, enabling fast and accurate 3D reconstructions of patient anatomy. Surgical navigation systems utilizing XR are already in development, showing an early signal towards improved patient outcomes when used for hepato-biliary surgery. Live visualization of hepato-biliary anatomy in the operating theatre is likely to improve operative safety and performance. The technological advances in AI and XR provide new applications in pre-operative planning with potential for patient benefit. Their use in surgical simulation could accelerate learning curves for surgeons in training. Future research must focus on standardization of AI and XR study reporting, robust databases that are ethically and data protection-compliant, and development of inter-disciplinary tools for various healthcare applications and systems. Full article

Healthcare data rapidly increases, and patients seek customized, effective healthcare services. Big data and machine learning (ML) enabled smart healthcare systems hold revolutionary potential. Unlike previous reviews that separately address AI or big data, this work synthesizes their convergence through real-world case studies, cross-domain ML applications, and a critical discussion on ethical integration in smart diagnostics. The review focuses on the role of big data analysis and ML towards better diagnosis, improved efficiency of operations, and individualized care for patients. It explores the principal challenges of data heterogeneity, privacy, computational complexity, and advanced methods such as federated learning (FL) and edge computing. Applications in real-world settings, such as disease prediction, medical imaging, drug discovery, and remote monitoring, illustrate how ML methods, such as deep learning (DL) and natural language processing (NLP), enhance clinical decision-making. A comparison of ML models highlights their value in dealing with large and heterogeneous healthcare datasets. In addition, the use of nascent technologies such as wearables and Internet of Medical Things (IoMT) is examined for their role in supporting real-time data-driven delivery of healthcare. The paper emphasizes the pragmatic application of intelligent systems by highlighting case studies that reflect up to 95% diagnostic accuracy and cost savings. The review ends with future directions that seek to develop scalable, ethical, and interpretable AI-powered healthcare systems. It bridges the gap between ML algorithms and smart diagnostics, offering critical perspectives for clinicians, data scientists, and policymakers. Full article

Artificial intelligence (AI) agents are increasingly shaping vital sectors of society, including healthcare, education, supply chains, and finance. As their influence grows, AI alignment research plays a pivotal role in ensuring these systems are trustworthy, transparent, and aligned with human values. Leveraging blockchain technology, proven over the past decade in enabling transparent, tamper-resistant distributed systems, offers significant potential to strengthen AI alignment. However, despite its potential, the current AI alignment literature has yet to systematically explore the effectiveness of blockchain in facilitating secure and ethical behavior in AI agents. While existing systematic literature reviews (SLRs) in AI alignment address various aspects of AI safety and AI alignment, this SLR specifically examines the gap at the intersection of AI alignment, blockchain, and ethics. To address this gap, this SLR explores how blockchain technology can overcome the limitations of existing AI alignment approaches. We searched for studies containing keywords from AI, blockchain, and ethics domains in the Scopus database, identifying 7110 initial records on 28 May 2024. We excluded studies which did not answer our research questions and did not discuss the thematic intersection between AI, blockchain, and ethics to a sufficient extent. The quality of the selected studies was assessed on the basis of their methodology, clarity, completeness, and transparency, resulting in a final number of 46 included studies, the majority of which were journal articles. Results were synthesized through quantitative topic analysis and qualitative analysis to identify key themes and patterns. The contributions of this paper include the following: (i) presentation of the results of an SLR conducted to identify, extract, evaluate, and synthesize studies on the symbiosis of AI alignment, blockchain, and ethics; (ii) summary and categorization of the existing benefits and challenges in incorporating blockchain for AI alignment within the context of ethics; (iii) development of a framework that will facilitate new research activities; and (iv) establishment of the state of evidence with in-depth assessment. The proposed blockchain-based AI alignment framework in this study demonstrates that integrating blockchain with AI alignment can substantially enhance robustness, promote public trust, and facilitate ethical compliance in AI systems. Full article

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article

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

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

The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. Full article

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

Smart home technology enhances the quality of life, particularly with respect to in-home care and health monitoring. While video-based methods provide accurate behavior analysis, privacy concerns drive interest in non-visual alternatives. This study proposes a Bluetooth Low Energy (BLE)-enabled indoor positioning and behavior recognition system, integrating machine learning techniques to support sustainable and privacy-preserving health monitoring. Key optimizations include: (1) a vertically mounted Data Collection Unit (DCU) for improved height positioning, (2) synchronized data collection to reduce discrepancies, (3) Kalman filtering to smooth RSSI signals, and (4) AI-based RSSI analysis for enhanced behavior recognition. Experiments in a real home environment used a smart wristband to assess BLE signal variations across different activities (standing, sitting, lying down). The results show that the proposed system reliably tracks user locations and identifies behavior patterns. This research supports elderly care, remote health monitoring, and non-invasive behavior analysis, providing a privacy-preserving solution for smart healthcare applications. Full article

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

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

The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals. Full article

Background: Developing countries face significant challenges in accessing high-quality pharmaceutical products due to resource constraints, limited regulatory capacity, and market dynamics that often prioritize cost over quality. This review addresses the critical gap in regulatory frameworks that fail to ensure pharmaceutical quality equity between developed and developing nations. Objective: This comprehensive review examines a novel dual-pathway regulatory framework that leverages stringent regulatory authority (SRA) approvals, artificial intelligence-based evaluation systems, and harmonized pricing mechanisms to ensure pharmaceutical quality equity across global markets. Methods: A comprehensive systematic analysis of current regulatory challenges, proposed solutions, and implementation strategies was conducted through an extensive literature review (202 sources, 2019–2025), expert consultation on regulatory science, AI implementation in healthcare, and pharmaceutical policy development. The methodology included an analysis of regulatory precedents, an economic impact assessment, and a feasibility evaluation based on existing technological implementations. Results: The proposed framework addresses key regulatory capacity gaps through two complementary pathways: Pathway 1 enables same-batch distribution from SRA-approved products with pricing parity mechanisms. At the same time, Pathway 2 provides independent evaluation using AI-enhanced systems for differentiated products. Key components include indigenous AI development, which requires systematic implementation over 4–6 years across three distinct stages, outsourced auditing frameworks that reduce costs by 40–50%, and quality-first principles that categorically reject cost-based quality compromises. Implementation analysis demonstrates a potential for achieving a 90–95% quality standardization, accompanied by a 200–300% increase in regulatory evaluation capability. Conclusions: This framework has the potential to significantly improve pharmaceutical quality and access in developing countries while maintaining rigorous safety and efficacy standards through innovative regulatory approaches. The evidence demonstrates substantial public health benefits with projected improvements in population access (85–95% coverage), treatment success rates (90–95% efficacy), and economic benefits (USD 15–30 billion in system efficiencies), providing a compelling case for implementation that aligns with global scientific consensus and Sustainable Development Goal 3.8. Full article

Objectives: With the growing importance of mobile technology and artificial intelligence (AI) in healthcare, the development of automated cardiac diagnostic systems has gained strategic significance. This review aims to summarize the current state of knowledge on the use of AI in the analysis of electrocardiographic (ECG) signals obtained from wearable devices, particularly smartwatches, and to outline perspectives for future clinical applications. Methods: A narrative literature review was conducted using PubMed, Web of Science, and Scopus databases. The search focused on combinations of keywords related to AI, ECG, and wearable technologies. After screening and applying inclusion criteria, 152 publications were selected for final analysis. Conclusions: Modern AI algorithms—especially deep neural networks—show promise in detecting arrhythmias, heart failure, prolonged QT syndrome, and other cardiovascular conditions. Smartwatches without ECG sensors, using photoplethysmography (PPG) and machine learning, show potential as supportive tools for preliminary atrial fibrillation (AF) screening at the population level, although further validation in diverse real-world settings is needed. This article explores innovation trends such as genetic data integration, digital twins, federated learning, and local signal processing. Regulatory, technical, and ethical challenges are also discussed, along with the issue of limited clinical evidence. Artificial intelligence enables a significant enhancement of personalized, mobile, and preventive cardiology. Its integration into smartwatch ECG analysis opens a path toward early detection of cardiac disorders and the implementation of population-scale screening approaches. Full article