Search Results (539)

In the healthcare and clinical domain, artificial intelligence (AI) is evolving from earlier models that primarily predicted outcomes or generated content toward agentic AI systems that demonstrate the capability to make decisions and complete tasks autonomously. Previous research on AI has contributed significantly to disease identification, deep learning applications, large language models (LLMs), and generative AI. These systems primarily function as assistive tools, as they generate text or predictions without directly interacting with clinical infrastructures. Therefore, recent research trends are increasingly oriented toward agentic AI systems that extend beyond traditional predictive and generative model performance. This manuscript provides a detailed review of the current state of agentic AI, starting with the evolution of AI and the concept of a medical agent. A medical agent refers to an intelligent AI system designed to assist in clinical or administrative tasks by analyzing data, supporting decision making, and interacting with healthcare environments. Its underlying agentic AI architecture integrates planning, memory, reasoning, and environmental interaction to enable autonomous tool use, multi-agent collaboration, and continuous perception decision action loops across diverse healthcare applications and clinical workflows. The review further examines safety mechanisms, including human-in-the-loop oversight, self-verification strategies, and regulatory alignment frameworks, which are designed to ensure reliability, accountability, compliance, and safe deployment in regulated healthcare environments. Our findings indicate that a large number of AI agents have been introduced in various manuscripts for healthcare applications; however, fully autonomous systems remain challenging to achieve, as AI still faces several limitations related to reliability, interpretability, data dependency, and integration within complex clinical workflows. In response to these challenges, this survey shifts the focus from task-specific model performance to system-level autonomy and workflow orchestration, providing a structured foundation for understanding the design, deployment, governance, and limitations of agentic AI systems in modern healthcare ecosystems. Full article

Access to neurological care remains profoundly unequal worldwide, driven by the increasing burden of chronic and neurodegenerative disorders and a persistent shortage of specialist neurologists. Tele-neurology has emerged as a promising strategy to improve access to neurological expertise, while recent advances in artificial intelligence (AI) have expanded its capabilities beyond remote consultation toward data-driven diagnosis, monitoring, and clinical decision support. This narrative review critically synthesizes current evidence on the evolution of tele-neurology and the emerging role of AI across multiple domains of neurological care. The review examines AI-enhanced diagnostic applications, including neuroimaging, electroencephalography, and digital biomarkers, as well as AI-assisted remote monitoring, patient engagement, and predictive analytics in disorders such as stroke, epilepsy, Parkinson’s disease, multiple sclerosis, dementia, headache disorders, and neuromuscular diseases. Available evidence suggests that tele-neurology can achieve clinical outcomes comparable to in-person care in selected settings while improving accessibility, continuity of care, and patient satisfaction. AI applications have shown promising early results in image interpretation, automated EEG analysis, remote disease monitoring, and individualized risk prediction. Despite these advances, important challenges remain. Limitations include constraints in remote neurological examination, evidence gaps regarding clinical validation, unequal access to digital infrastructure, data governance and cybersecurity concerns, fragmented regulatory frameworks, and difficulties integrating AI into routine clinical workflows. Furthermore, the clinical maturity of AI applications varies substantially, with many systems remaining at the proof-of-concept or early validation stage. To make this variation explicit, we introduce a transparent, criteria-based three-tier classification of the clinical practicability of AI applications, graded by strength of evidence, degree of validation, and real-world integration. Tele-neurology is increasingly evolving toward hybrid models that combine in-person neurological assessment with digitally enabled longitudinal monitoring and AI-supported decision tools. Future progress will depend on rigorous clinical validation, equitable implementation strategies, integration into healthcare systems, and the development of multimodal AI approaches that combine clinical, imaging, electrophysiological, and digital biomarker data to support personalized neurological care. Full article

Artificial intelligence (AI) has rapidly emerged as a transformative tool in virology, offering new opportunities for the detection, classification, and surveillance of viral pathogens. Recent advances in machine learning, deep neural networks, and multimodal data analysis now enable the identification of viral signatures from genomic sequences, medical images, environmental samples, and social-media-derived epidemiological signals. This review provides a comprehensive overview of state-of-the-art AI methodologies applied to viral pathogen research, with a particular focus on image-based diagnostics, automated quality assessment of virology-related digital content, and predictive modelling for outbreak monitoring. We discuss how convolutional and transformer-based architectures are being used to classify infected tissues, detect viral particles, and support laboratory workflows. Furthermore, we highlight the emerging role of AI in evaluating the reliability of user-generated images and short videos related to infectious diseases, an area increasingly relevant in the age of misinformation. Challenges such as dataset bias, limited annotated virological images, ethical concerns, and the need for standardized quality-assessment pipelines are critically examined. Finally, we outline future research directions, including hybrid AI–biological models, AI-supported viral surveillance in healthcare environments, and the integration of explainable AI to enhance clinical trust. Full article

Background/Objectives: Medical devices are essential to modern healthcare, but their adoption is often driven by regulatory conformity, clinical enthusiasm, procurement pressures and vendor-led innovation rather than systematic evaluation of comparative value. CE marking and related regulatory mechanisms are necessary for market access; however, they do not determine whether a device improves patient-related outcomes compared with existing alternatives, whether its benefits justify its total costs, or whether it can be implemented safely in routine care. This narrative review examines why medical devices require a dedicated life-cycle health technology assessment (HTA) approach and proposes an operational framework linking assessment to adoption, evidence generation, reassessment and disinvestment. Methods: A structured targeted search covered peer-reviewed literature and policy or institutional documents addressing HTA, medical devices, regulation, economic evaluation, real-world evidence, hospital-based HTA, procurement digital and AI-enabled devices, patient involvement and post-market reassessment. Results: Medical devices differ from pharmaceuticals through user dependence, learning curves, procedure dependence, short product life cycles, incremental modification, heterogeneous comparators, limited randomized evidence and hidden life-cycle costs. These features create clinical, economic, organizational and implementation uncertainty after market entry. The proposed model specifies six linked phases: horizon scanning and early dialogue, pre-adoption appraisal, an explicit adoption decision, controlled implementation, real-world monitoring and scheduled or trigger-based reassessment leading to continuation, scale-up, restriction, or disinvestment. Practical constraints include fragmented data infrastructure, the cost of maintaining registries and residual confounding in real-world evidence. Conclusions: Medical device HTA should move beyond one-time pre-adoption assessment toward a decision-linked life-cycle model that integrates comparative value, patient and public involvement, procurement, implementation governance, real-world evidence, version monitoring, reassessment and disinvestment. This approach can support responsible innovation, patient safety, transparent procurement and sustainable health-system value. Full article

Background: Artificial intelligence (AI) is increasingly proposed to augment early-stage assistance for survivors of sexual and gender-based violence (GBV), including intimate partner and domestic violence, across crisis hotlines, specialist services, digital reporting channels, legal support tools and healthcare pathways. However, the scope, maturity and evaluative strength of the peer-reviewed evidence remain uncertain. We aimed to map the application domains, evaluative maturity, and implementation and safety gaps of this evidence base. Methods: We conducted a scoping review reported according to the PRISMA Extension for Scoping Reviews (PRISMA-ScR), using a Population–Concept–Context framework focused on AI-enabled first-response and early support. Searches in Scopus, Web of Science Core Collection and PubMed were supplemented by targeted searches of IEEE Xplore and ACM Digital Library. Records were screened against predefined criteria, charted using a structured form and synthesised descriptively. Results: Original searches yielded 187 records and 21 included sources of evidence. The supplementary search identified 539 records/candidates; 27 full texts were assessed and 6 additional sources met eligibility criteria, yielding 27 included sources of evidence. Evidence covered survivor-facing conversational support; screening and triage in emergency and specialist services; social-triage and online disclosure models; survivor-informed help-seeking and chatbot design; legal/support routing; and enabling modalities such as speech-based approaches. Most sources reported technical performance, usability, acceptability or systems-audit findings, while no workflow-integrated evaluation was identified and survivor-centred effectiveness outcomes, service uptake and adverse-event monitoring were rarely reported. Conclusions: The evidence remains heterogeneous and early-stage, with limited support for service-integrated effectiveness or safety. Included sources more often assessed models, interfaces or prototypes than downstream pathway outcomes. The findings support cautious, pathway-aware interpretation and identify recurring concerns regarding escalation, accountability, equity, digital trace safety and human handover. The proposed practice considerations and outcome domains are author-informed priorities for future pilot and implementation studies, not validated guidelines. Full article

This paper presents a systematic literature review examining how blockchain technologies can enhance the security and performance of multi-cloud systems. Multi-cloud architectures offer resilience, scalability, and flexibility; however, they also pose complex security challenges related to APIs, service-level agreements (SLAs), orchestration, and authentication. The promise of blockchain technology to improve the security and transparency of numerous applications, including cloud storage systems, has attracted considerable attention in recent years. Much research has focused on decentralized storage in cloud environments, spanning supply chains, FinTech, healthcare, and education. Still, the integration of blockchain with the cloud and its potential to enhance security and performance warrant an in-depth study. Using the PRISMA methodology, a structured search was conducted across six major scientific databases, including IEEE, ACM Digital Library, ScienceDirect, Scopus, Web of Science, and IJIMAI. Twenty-four primary papers published between 2019 and 2025 were selected for analysis after clear inclusion and exclusion criteria were applied. This review examines the security dimensions in multi-cloud environments—architectural vulnerabilities, API security, authentication, orchestration and automation vulnerabilities, SLAs, and cybersecurity compliance issues—in relation to blockchain technology. Based on the identified gaps, we propose a SOC-LLM-augmented security architecture that integrates blockchain-based evidence integrity, statistical anomaly detection, machine learning, large language models, and autonomous AI agents to enable intelligent attack detection and response. The proposed framework introduces specialized agents for detection, correlation, threat intelligence retrieval, blockchain evidence validation, explanation generation, and response planning. The analysis shows that integrating SOC-LLM capabilities with blockchain can move multi-cloud security from passive auditability toward proactive, explainable, and human-in-the-loop cyber defense. Finally, this paper discusses open challenges, including LLM hallucination, data scarcity, real-time scalability, evaluation standardization, and trustworthy deployment in critical multi-cloud infrastructures. The study’s conclusion highlights research gaps and suggests future lines of inquiry concerning scalable blockchain architectures and the incorporation of AI for proactive cloud security monitoring. Full article

Cardiovascular disease remains the leading global health challenge, claiming approximately 19.8 million lives annually. The convergence of wearable technology and artificial intelligence represents a transformative shift in cardiovascular healthcare, enabling continuous real-time monitoring beyond conventional clinical settings. This narrative review synthesises current evidence on integrating consumer-grade and medical-grade wearable devices with AI algorithms for continuous cardiovascular monitoring applications, with particular attention to real-world translational applicability and global health equity. This review examined the technological landscape of wearable cardiovascular monitoring devices, including smartwatches with photoplethysmography and electrocardiogram capabilities, continuous cardiac monitoring patches, and emerging biosensor technologies. Also, the review explored AI methodologies, particularly machine learning and deep learning architectures, employed in processing complex physiological data streams from these devices. Clinical applications demonstrate impressive capabilities: arrhythmia detection with sensitivity rates exceeding 98%, continuous blood pressure monitoring through cuffless technologies, heart failure decompensation prediction, and cardiovascular risk stratification. However, substantial challenges persist, including data quality assurance, algorithm interpretability, regulatory compliance, and seamless clinical workflow integration. Privacy concerns, health disparities in algorithm performance, and the need for robust validation across diverse populations remain critical considerations. AI-enhanced wearable systems hold considerable potential for shifting cardiovascular care from reactive treatment paradigms towards predictive, preventive, and precision medicine approaches. Future directions include edge computing architectures, federated learning approaches, personalised AI models, enhanced interoperability with electronic health records, and expansion to resource-limited settings, ultimately improving patient outcomes whilst reducing healthcare costs. Full article

The electrocardiogram (ECG) is essential for cardiovascular diagnosis but limited by inter-observer variability, low sensitivity for subclinical disease, and labor-intensive telemonitoring analysis. Artificial intelligence (AI), particularly deep learning, addresses these constraints by extracting high-dimensional patterns that correlate with arrhythmias, structural abnormalities, and systemic conditions. This integrative review synthesizes recent advances in AI-enabled ECG, covering technical foundations—including foundation models and validation strategies—and clinical applications, such as arrhythmia detection, structural heart disease identification, and digital biomarker derivation. We discuss emerging trends like self-supervised learning, multimodal integration, generative models, and explainability techniques. Furthermore, we tackle critical challenges regarding generalizability, algorithmic bias, privacy, and regulatory systems. Finally, we outline research priorities, including curated open datasets, and deployment in resource-constrained settings. With stringent validation, transparent governance, and human-centered design, AI-ECG has the potential to enhance cardiovascular diagnostics and clinical outcomes across a variety of healthcare settings. Full article

Background/Objectives: Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality worldwide, and accurate histopathological tissue segmentation is critical for timely and reliable diagnosis. Healthcare systems represent complex adaptive environments where diagnostic tools must function reliably across heterogeneous clinical settings, varying staining protocols, and resource-constrained infrastructures. However, existing deep learning segmentation models often require substantial computational resources, limiting their deployment in such settings. This study proposes a novel, resource-efficient colorectal histopathology segmentation network (RCHS-Net) designed for robust clinical deployment across diverse and resource-constrained healthcare environments. Methods: RCHS-Net employs a compact multi-scale encoder with channel recalibration blocks, a gland context module (GCM) with three parallel atrous convolutions and lightweight self-attention for multi-scale contextual feature extraction, and a feature pyramid decoder (FPD) for fine-grained spatial reconstruction. To address the demands of real-world healthcare systems, feature-wise linear modulation (FiLM) conditioning enables class-aware segmentation across multiple tissue categories, while MixStyle augmentation improves stain domain generalization across heterogeneous laboratory and scanner conditions. Results: The model was evaluated on two publicly available benchmark datasets: the EBHI-Seg dataset and the GlaS dataset. On EBHI-Seg, RCHS-Net achieved a mean Dice coefficient of 95.20% and a mean IoU of 91.10% across six colorectal tissue classes, with only 243,226 trainable parameters. On the GlaS benchmark, RCHS-Net attained a Dice score of 93.39% and an IoU of 88.32%, outperforming state-of-the-art methods. Conclusions: RCHS-Net demonstrates that high-accuracy histopathology segmentation can be achieved with a compact architecture, offering a scalable and practical solution for AI-assisted cancer diagnosis across the complex, heterogeneous conditions of real-world healthcare systems, supporting scalable and equitable cancer diagnostics globally. Full article

The medical triage platform, by integrating clinical and informatics components, aims to optimize patient flow in emergency and outpatient services. Case variability and the need for rapid prioritization can significantly influence medical decisions and subsequent patient management. In this regard, we propose an integrated software platform consisting of a triage component for initial patient assessment and a clinical management component for monitoring medical history, investigations, and the therapeutic pathway. The system allows for the input of identification data, symptomatology, vital signs, and clinical observations, generating a priority level and an AI-assisted anamnesis. Data is stored in a relational database, with each patient associated with a unique code, enabling rapid file access and longitudinal case tracking. Beyond its current capabilities, the platform features tools for logging investigations and managing outpatient discharges. Future updates are set to induce specialized modules for admissions, diagnostics and protocol-driven treatments. This systematic approach allows for a more standardized triage process, a significant reduction in administrative slip-ups and a boost in overall healthcare productivity. Full article

The global buildings and construction sector remains a dominant contributor to anthropogenic climate change, and deep decarbonization has positioned digital twin technology as a transformative pathway for intelligent building energy management. Despite considerable research momentum, the field lacks a coherent synthesis mapping AI capabilities onto the full digital twin lifecycle—from sensor-driven calibration through real-world deployment to district-scale operation. This review addresses this gap through six objectives: analyzing AI-enhanced modeling approaches for building digital twins; examining data infrastructure and interoperability requirements; evaluating validation, calibration, and uncertainty quantification practices; synthesizing real-world implementation evidence across diverse building typologies; assessing integration with renewable energy systems and smart grids; and identifying challenges, research gaps, and a strategic deployment roadmap. Physics-based, data-driven, and hybrid modeling strategies occupy distinct and complementary roles. Physics-informed surrogate models preserve thermodynamic interpretability while reducing computational overhead; deep learning architectures—including recurrent networks and reinforcement learning agents—deliver adaptive control; and federated learning frameworks enable privacy-preserving optimization across distributed building portfolios. Rigorous multi-metric validation aligned with established calibration standards proves essential for trustworthy deployment, while Bayesian and ensemble-based uncertainty quantification methods emerge as indispensable components of operationally credible digital twins. Evidence from real-world deployments in residential, commercial, healthcare, and industrial facilities confirms that AI-powered digital twins consistently deliver substantial energy savings and measurable improvements in occupant comfort. Scaling to district and urban levels introduces challenges in data governance, computational architecture, and multi-stakeholder coordination, yet federated digital twin frameworks are beginning to demonstrate viable pathways. The paper concludes with a decade-long strategic roadmap spanning technological maturation, market development, regulatory alignment, and decarbonization impact—positioning AI-enhanced digital twins not as incremental optimization tools, but as the foundational infrastructure for the coordinated transformation of the global building stock. Full article

Smart Hospitals integrated within 6G edge networks aim to enhance hospital connectivity and operational efficiency by enabling intelligent and personalized e-health services and applications while optimizing resource utilization and maintaining a high degree of autonomy. Nevertheless, the interconnectivity and 6G integration, which comprise core components of Smart Hospitals, are susceptible to a wide range of security threats, posing significant risks to the confidentiality, integrity, and availability of hospital data and operations. Given that security is a critical concern for Smart Hospitals, there is an urgent need to develop novel security mechanisms to safeguard these environments within 6G edge networks. In particular, this work highlights how defining 6G characteristics, such as Ultra-Reliable Low-Latency Communications, massive IoMT connectivity, distributed edge intelligence, and AI-native network operation, not only enable next-generation hospital services but also reshape the security and privacy threat landscape and the requirements of mitigation mechanisms. In this context, the first essential step is to comprehensively understand both existing and emerging threats targeting Smart Hospitals in the 6G edge network ecosystem. Therefore, this article provides a categorization of security and privacy attacks based on their primary targets. Moreover, it presents a survey of mitigation techniques derived from recent literature, specifically designed to counter threats facing Smart Hospitals in 6G edge networks. The intent is to establish a foundation that supports ongoing research towards the development of effective, 6G-aware security countermeasures capable of protecting Smart Hospitals under the stringent latency, scalability, and reliability requirements of future healthcare environments. Full article

Cardiac arrhythmias complicate 20–50% of surgical procedures and contribute substantially to perioperative morbidity, mortality, and healthcare costs, with postoperative atrial fibrillation (POAF) being the most frequent form. Their genesis reflects the convergence of surgical stress, anaesthetic agents, autonomic imbalance, systemic inflammation, and electrolyte disturbances, explaining the limited efficacy of single-mechanism interventions. This narrative review synthesises contemporary evidence on pathophysiology, risk stratification, prevention, acute management, and emerging technologies, emphasising individualised, patient-tailored approaches. MEDLINE, Embase, and Cochrane CENTRAL were searched (January 2010–January 2026), prioritising randomised trials, meta-analyses, and guidelines. Contemporary risk stratification integrates clinical scores, biomarkers, and electrocardiographic parameters; machine-learning models show moderate discrimination (pooled AUC 0.84) and may enable more personalised prediction pending external validation. Evidence-based prophylaxis—beta-blockade, magnesium, selective amiodarone, and emerging anti-inflammatory strategies such as colchicine—reduces POAF in high-risk populations, while acute management is guided by haemodynamic status and individual risk. Anticoagulation follows CHA₂DS₂-VASc stratification, although optimal timing and duration remain undefined. Wearable monitoring, AI-based detection, and atrial-selective agents show clinical promise. Systematic, personalised integration of risk assessment, prophylaxis, monitoring, and management offers the clearest path to reducing arrhythmia-associated morbidity. Full article

As artificial intelligence becomes increasingly embedded in healthcare operations, governance can no longer be treated solely as a static set of principles or regulatory requirements. This study proposes a Human-Centered Experience Engineering (HCEE)-based healthcare AI governance architecture designed as a closed-loop operational control system for stabilizing and adaptively managing AI-enabled healthcare systems. Rather than treating patient and employee experience as downstream outcomes, the framework repositions them as governance input signals, operationalized as the Patient Experience Index (PXI) and Employee Experience Index (EXI). The architecture integrates Policy, Governance, Control, and Experience through recurrent feedback, trigger-control rules, and adaptive learning mechanisms that translate experiential signals into ongoing operational adjustment. To examine how this architecture affects system behavior, agent-based simulation compares four scenarios: S1 (no governance), S2 (sense-only), S3 (full HCEE closed loop), and S4 (full HCEE under exogenous stress). Results show a consistent pattern of S1 < S2 < S3 in both PXI and EXI, indicating that sensing alone yields limited improvement, whereas feedback-coupled sensing, control, and learning produce stronger stabilization and performance gains. In S4, the same architecture demonstrates recovery, re-stabilization, and adaptive reinforcement under shock. These findings suggest that effective healthcare AI governance depends on whether feedback loops are architected to function operationally. These results are based on synthetic experience indices and are interpreted as conceptual, mechanism-level evidence. Full article

Traditional biomechanical analysis is constrained by high capital costs and the physical limitations imposed by markers, posing significant barriers to clinical adoption. This review evaluates the emergence of artificial intelligence (AI)-based markerless motion capture (MMC) as a transformative approach for democratizing movement science in clinical rehabilitation. The discussion outlines the progression from legacy geometric visual hulls to advanced deep learning architectures, with particular focus on YOLO-based two-dimensional detection and spatio-temporal transformer models for three-dimensional pose estimation. Evidence indicates that multi-camera MMC frameworks achieve research-grade positional accuracy (16–34 mm Mean Per-Joint Position Error—MPJPE), while monocular systems provide sufficient sensitivity (82–88%) for longitudinal monitoring of geriatric fall risk and stroke recovery. While challenges persist in achieving precise axial rotation measurement, integrating real-time signal refinement enables objective and ecologically valid assessments in community-based healthcare settings. This technological advancement redefines movement analysis, shifting it from a laboratory-bound procedure to a widely accessible and interoperable diagnostic tool. Full article