Search Results (3,497)

During the last decade, artificial intelligence (AI) has enabled key technological innovations within the modern dementia and frailty healthcare and prevention landscape. This has boosted the impact of technology in the clinical setting, enabling earlier diagnosis with improved specificity and sensitivity, leading to accurate and time-efficient support that has driven the development of preventative interventions minimizing the risk and rate of progression. Background/Objectives: The rapid ageing of the European population places a substantial strain on the current healthcare system and imposes several challenges. COMFORTage is the joint effort of medical experts (i.e., neurologists, psychiatrists, neuropsychologists, nurses, and memory clinics), social scientists and humanists, technical experts (i.e., data scientists, AI experts, and robotic experts), digital innovation hubs (DIHs), and living labs (LLs) to establish a pan-European framework for community-based, integrated, and people-centric prevention, monitoring, and progression-managing solutions for dementia and frailty. Its main goal is to introduce an integrated and digitally enabled framework that will facilitate the provision of personalized and integrated care prevention and intervention strategies on dementia and frailty, by piloting novel technologies and producing quantified evidence on the impact to individuals’ wellbeing and quality of life. Methods: A robust and comprehensive design approach adopted through this framework provides the guidelines, tools, and methodologies necessary to empower stakeholders by enhancing their health and digital literacy. The integration of the initial information from 13 pilots across 8 European countries demonstrates the scalability and adaptability of this approach across diverse healthcare systems. Through a systematic analysis, it aims to streamline healthcare processes, reduce health inequalities in modern communities, and foster healthy and active ageing by leveraging evidence-based insights and real-world implementations across multiple regions. Results: Emerging technologies are integrated with societal and clinical innovations, as well as with advanced and evidence-based care models, toward the introduction of a comprehensive global coordination framework that: (a) improves individuals’ adherence to risk mitigation and prevention strategies; (b) delivers targeted and personalized recommendations; (c) supports societal, lifestyle, and behavioral changes; (d) empowers individuals toward their health and digital literacy; and (e) fosters inclusiveness and promotes equality of access to health and care services. Conclusions: The proposed framework is designed to enable earlier diagnosis and improved prognosis coupled with personalized prevention interventions. It capitalizes on the integration of technical, clinical, and social innovations and is deployed in 13 real-world pilots to empirically assess its potential impact, ensuring robust validation across diverse healthcare settings. Full article

Background: Zebrafish-based drug discovery systems provide significant advantages over mammalian models for high-throughput in vivo screening. Among these, the NF-κB eGFP reporter system significantly enhances drug discovery in zebrafish by enabling real-time, high-resolution monitoring of pathway activity in live organisms, thereby streamlining mechanistic studies and high-throughput screening. Methods: We developed a novel AI (Quantifish and Orange software)-based zebrafish precision individualized 96-well ZF plates (0–7 dpf) and individualized MT tanks (8 dpf–4 mpf) protocol for the transposon-mediated transgenesis of the NFκB eGFP reporter system. Results: One-cell stage embryos were administered NFκB reporter construct and Tol2 transposase mRNA via microinjection and transferred to separate wells of a 96-well ZF plate. Bright-field and fluorescence images of each well were captured at 5 dpf in the F0, F1, and F2 generations using the automated confocal high-content imager CQ1. The Quantifish software was used for the automated detection and segmentation of zebrafish larval fluorescence intensity in specific regions of interest. Quantitative data on the fluorescence intensity and distribution patterns were measured in Quantifish, and advanced statistical and machine learning methods were applied using Orange. Imaging data with eGFP expression results were assessed to evaluate the efficiency of the transgenic protocol. Discussion: This AI-enhanced precision protocol allows for high-throughput screening and quantitative analysis of NFκB reporter transgenesis in zebrafish, enabling the efficient identification and characterization of stable transgenic lines that exhibit tissue-specific expression of the NF-κB reporter, such as lines with induced expression restricted to the retina following LPS stimulation. This approach streamlines the evaluation of regulatory elements, enhances data consistency, and reduces animal use, making it a valuable tool for zebrafish drug discovery. Full article

Background/Objectives: Bones are essential components of the human body, providing structural support, enabling mobility, storing minerals, and protecting internal organs. Bone fractures (BFs) are common injuries that result from excessive physical force and can lead to serious complications, including bleeding, infection, impaired oxygenation, and long-term disability. Early and accurate identification of fractures through radiographic imaging is critical for effective treatment and improved patient outcomes. However, manual evaluation of X-rays is often time-consuming and prone to diagnostic errors due to human limitations. To address this, artificial intelligence (AI), particularly deep learning (DL), has emerged as a powerful tool for enhancing diagnostic precision in medical imaging. Methods: This research introduces a novel convolutional neural network (CNN) model, the Multi-Level Feature Fusion Network (MLFNet), designed to capture and integrate both low-level and high-level image features. The model was evaluated using the Bone Fracture Multi-Region X-ray (BFMRX) dataset. Preprocessing steps included image normalization, resizing, and contrast enhancement to ensure stable convergence, reduce sensitivity to lighting variations in radiographic images, and maintain consistency. Ablation studies were conducted to assess architectural variations, confirming the model’s robustness and generalizability across data distributions. MLFNet’s high accuracy, interpretability, and efficiency make it a promising solution for clinical deployment. Results: MLFNet achieved an impressive accuracy of 99.60% as a standalone model and 98.81% when integrated into hybrid ensemble architectures with five leading pre-trained DL models. Conclusions: The proposed approach supports timely and precise fracture detection, optimizing the diagnostic process and reducing healthcare costs. This approach offers significant potential to aid clinicians in fields such as orthopedics and radiology, contributing to more equitable and effective patient care. Full article

Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption. Full article

Despite rapid progress in natural language processing, current interactive AI systems continue to struggle with interpreting ambiguous, idiomatic, and contextually rich human language, a barrier to natural human–computer interaction. Many deployed applications, such as language games or educational tools, showcase surface-level adaptation but do not systematically probe or advance the deeper semantic understanding of user intent in open-ended, creative settings. In this paper, we present Semantrix, a web-based semantic word-guessing platform, not merely as a game but as a living testbed for evaluating and extending the semantic capabilities of state-of-the-art Transformer models in human-facing contexts. Semantrix challenges models to both assess the nuanced meaning of user guesses and generate dynamic, context-sensitive hints in real time, exposing the system to the diversity, ambiguity, and unpredictability of genuine human interaction. To empirically investigate how advanced semantic representations and adaptive language feedback affect user experience, we conducted a preregistered 2 × 2 factorial study (N = 42), independently manipulating embedding depth (Transformers vs. Word2Vec) and feedback adaptivity (dynamic hints vs. minimal feedback). Our findings revealed that only the combination of Transformer-based semantic modelling and adaptive hint generation sustained user engagement, motivation, and enjoyment; conditions lacking either component led to pronounced attrition, highlighting the limitations of shallow or static approaches. Beyond benchmarking game performance, we argue that the methodologies applied in platforms like Semantrix are helpful for improving machine understanding of natural language, paving the way for more robust, intuitive, and human-aligned AI approaches. Full article

The phosphoinositide 3-kinase (PI3K)/AKT signaling pathway is a crucial regulator of cellular metabolism, proliferation, and survival. It is frequently dysregulated in metabolic, cardiovascular, and neoplastic disorders. Despite the advancements in multi-omics technology, existing methods often fail to provide real-time, pathway-specific insights for precision medicine and drug repurposing. We offer Agentic RAG-Driven Multi-Omics Analysis (ARMOA), an autonomous, hypothesis-driven system that integrates retrieval-augmented generation (RAG), large language models (LLMs), and agentic AI to thoroughly analyze genomic, transcriptomic, proteomic, and metabolomic data. Through the use of graph neural networks (GNNs) to model complex interactions within the PI3K/AKT pathway, ARMOA enables the discovery of novel biomarkers, probable candidates for drug repurposing, and customized therapy responses to address the complexities of PI3K/AKT dysregulation in disease states. ARMOA dynamically gathers and synthesizes knowledge from multiple sources, including KEGG, TCGA, and DrugBank, to guarantee context-aware insights. Through adaptive reasoning, it gradually enhances predictions, achieving 91% accuracy in external testing and 92% accuracy in cross-validation. Case studies in breast cancer and type 2 diabetes demonstrate that ARMOA can identify synergistic drug combinations with high clinical relevance and predict therapeutic outcomes specific to each patient. The framework’s interpretability and scalability are greatly enhanced by its use of multi-omics data fusion and real-time hypothesis creation. ARMOA provides a cutting-edge example for precision medicine by integrating multi-omics data, clinical judgment, and AI agents. Its ability to provide valuable insights on its own makes it a powerful tool for advancing biomedical research and treatment development. Full article

The rapid integration of generative AI, particularly ChatGPT, into academic settings has prompted urgent questions regarding its impact on students’ psychological and academic outcomes. Although generative AI holds considerable potential to transform educational practices, its effects on individual traits such as self-control and academic wellbeing remain insufficiently explored. This study addresses this gap through a sequential two-phase design. In the first phase, the ChatGPT Usage Scale was adapted and validated for a Turkish university student population (N = 413). Using confirmatory factor analysis and item response theory, the scale was confirmed as a psychometrically valid and reliable one-factor instrument. In the second phase, a separate sample (N = 449) was used to examine the relationships between ChatGPT usage, self-control, and academic wellbeing through a mediation model. The findings revealed that higher ChatGPT usage was significantly associated with lower levels of both self-control and academic wellbeing. Additionally, mediation analysis demonstrated that self-control partially mediates the negative relationship between ChatGPT usage and academic wellbeing. The study concludes that while generative AI tools are valuable, their integration into education presents a double-edged sword, highlighting the critical need to foster students’ self-regulatory skills to ensure they can harness these tools responsibly without compromising their academic and psychological health. Full article

Embedded Artificial Intelligence (EAI) integrates AI technologies with resource-constrained embedded systems, overcoming the limitations of cloud AI in aspects such as latency and energy consumption, thereby empowering edge devices with autonomous decision-making and real-time intelligence. This review provides a comprehensive overview of this rapidly evolving field, systematically covering its definition, hardware platforms, software frameworks and tools, core algorithms (including lightweight models), and detailed deployment processes. It also discusses its widespread applications in key areas like autonomous driving and smart Internet of Things (IoT), as well as emerging directions. By analyzing its core challenges and innovative opportunities in algorithms, hardware, and frameworks, this review aims to provide relevant researchers and developers with a practical guidance framework, promoting technological innovation and adoption. Full article

This systematic review examines the methodologies and applications of integrated risk indicators in positron emission tomography (PET) radiopharmaceutical production, focusing on occupational, technological, and environmental risks. Conducted in accordance with PRISMA 2020 guidelines and utilizing the Ryyan software 2023 for article screening, the review synthesizes findings from 70 studies published between 2020 and 2025 in English and Spanish, including articles, conference papers, and reviews. The review was registered on PROSPERO (CRD420251078221). Key disciplines contributing to risk assessment frameworks include environmental science, occupational health and safety, civil engineering, mining engineering, maritime safety, financial/economic risk, and systems engineering. Predominant risk assessment methods identified are probabilistic modeling (e.g., Monte Carlo simulations), machine learning (e.g., neural networks), multi-criteria decision-making (e.g., AHP and TOPSIS), and failure mode and effects analysis (FMEA), each offering strengths, such as uncertainty quantification and systematic hazard identification, alongside limitations like data dependency and subjectivity. The review explores how frameworks from other industries can be adapted to address PET-specific risks, such as radiation exposure to workers, equipment failure, and waste management, and how studies integrate these factors into unified risk indicators using weighted scoring, probabilistic methods, and fuzzy logic. Gaps in the literature include limited stakeholder engagement, lack of standardized frameworks, insufficient real-time monitoring, and under-represented environmental risks. Future research directions propose developing PET-specific tools, integrating AI and IoT for real-time data, establishing standardized frameworks, and expanding environmental assessments to enhance risk management in PET radiopharmaceutical production. This review highlights the interdisciplinary nature of risk assessment and the critical need for comprehensive, tailored approaches to ensure safety and sustainability in this field. Full article

Background: With the advancement of AI-powered online tools, patients are increasingly turning to AI for guidance on healthcare-related issues. Methods: Acting as patients, we posed eight direct questions concerning a common clinical condition—liver cysts—to four AI chatbots: ChatGPT, Perplexity, Copilot, and Gemini. The responses were collected and compared both among the chatbots and with the current literature, including the most recent guidelines. Results: Overall, the responses from the four chatbots were generally consistent with the literature, with only a few inaccuracies noted. For questions addressing “grey areas” in clinical research, all chatbots provided generalized answers. ChatGPT, Copilot, and Gemini highlighted the lack of conclusive evidence in the literature, while Perplexity offered speculative correlations not supported by data. Importantly, all chatbots recommended consulting a healthcare professional. While Perplexity, Copilot, and Gemini included references in their responses, not all cited sources were academic or of medium/high evidence quality. An analysis of Flesch Readability Ease Scores and Estimated Reading Grade Levels indicated that ChatGPT and Gemini provided the most readable and comprehensible responses. Conclusions: The integration of chatbots into real-world healthcare scenarios requires thorough testing to prevent potentially serious consequences from misuse. While undeniably innovative, this technology presents significant risks if implemented improperly. Full article

With the rapid advancement of artificial intelligence (AI), its deep integration into corporate operations has become the key driver for firms to reconfigure factor resources, boost green total factor productivity, and achieve green transformation. This analysis empirically investigates the influence of AI on corporate green transformation using panel data of China’s listed companies from 2015 to 2022. This research employs a multidimensional fixed effects linear model to analyze the relationship, finding that AI significantly enhances corporate green transformation. Mechanism analysis reveals that AI promotes green transformation by enhancing firm research and development (R&D) and firm green innovation capabilities. Heterogeneity analysis shows that the positive impact of AI on corporate green transformation is more significant in the eastern region, post-COVID−19, and in low-pollution industries. The impact is also significantly and positively moderated by the development of the non-state-owned economy and the development degree of product markets. These findings suggest that AI is a critical tool for promoting sustainable economic growth and green transformation in businesses. Full article

Liposomes are nanoscale, spherical vesicles composed of phospholipid bilayers, typically ranging from 50 to 200 nm in diameter. Their unique ability to encapsulate both hydrophilic and hydrophobic molecules makes them powerful nanocarriers for drug delivery, diagnostics, and vaccine formulations. Several FDA-approved formulations such as Doxil^® (Baxter Healthcare Corporation, Deerfield, IL, USA), AmBisome^® (Gilead Sciences, Inc., Foster City, CA, USA), and Onivyde^® (Ipsen Biopharmaceuticals, Inc., Basking Ridge, NJ, USA) highlight their clinical significance. This review provides a comprehensive synthesis of how molecular dynamics (MD) simulations, particularly coarse-grained (CG) and atomistic approaches, advance our understanding of liposomal membranes. We explore key membrane biophysical properties, including area per lipid (APL), bilayer thickness, segmental order parameter ($S_{CD}$), radial distribution functions (RDFs), bending modulus, and flip-flop dynamics, and examine how these are modulated by cholesterol concentration, PEGylation, and curvature. Special attention is given to curvature-induced effects in spherical vesicles, such as lipid asymmetry, interleaflet coupling, and stress gradients across the leaflets. We discuss recent developments in vesicle modeling using tools such as TS2CG, CHARMM-GUI Martini Maker, and Packmol, which have enabled the simulation of large-scale, compositionally heterogeneous systems. The review also highlights simulation-guided strategies for designing stealth liposomes, tuning membrane permeability, and enhancing structural stability under physiological conditions. A range of CG force fields, MARTINI, SPICA, SIRAH, ELBA, SDK, as well as emerging machine learning (ML)-based models, are critically assessed for their strengths and limitations. Despite the efficiency of CG models, challenges remain in capturing long-timescale events and atomistic-level interactions, driving the development of hybrid multiscale frameworks and AI-integrated techniques. By bridging experimental findings with in silico insights, MD simulations continue to play a pivotal role in the rational design of next-generation liposomal therapeutics. Full article

Background/Objectives: Artificial Intelligence (AI) is improving dentistry through increased accuracy in diagnostics, planning, and workflow automation. AI tools, including machine learning (ML) and deep learning (DL), are being adopted in oral medicine to improve patient care, efficiency, and lessen clinicians’ workloads. AI in dentistry, despite its use, faces an issue of acceptance, with its obstacles including ethical, legal, and technological ones. In this article, a review of current AI use in oral medicine, new technology development, and integration barriers is discussed. Methods: A narrative review of peer-reviewed articles in databases such as PubMed, Scopus, Web of Science, and Google Scholar was conducted. Peer-reviewed articles over the last decade, such as AI application in diagnostic imaging, predictive analysis, real-time documentation, and workflows automation, were examined. Besides, improvements in AI models and critical impediments such as ethical concerns and integration barriers were addressed in the review. Results: AI has exhibited strong performance in radiographic diagnostics, with high accuracy in reading cone-beam computed tomography (CBCT) scan, intraoral photographs, and radiographs. AI-facilitated predictive analysis has enhanced personalized care planning and disease avoidance, and AI-facilitated automation of workflows has maximized administrative workflows and patient record management. U-Net-based segmentation models exhibit sensitivities and specificities of approximately 93.0% and 88.0%, respectively, in identifying periapical lesions on 2D CBCT slices. TensorFlow-based workflow modules, integrated into vendor platforms such as Planmeca Romexis, can reduce the processing time of patient records by a minimum of 30 percent in standard practice. The privacy-preserving federated learning architecture has attained cross-site model consistency exceeding 90% accuracy, enabling collaborative training among diverse dentistry clinics. Explainable AI (XAI) and federated learning have enhanced AI transparency and security with technological advancement, but barriers include concerns regarding data privacy, AI bias, gaps in AI regulating, and training clinicians. Conclusions: AI is revolutionizing dentistry with enhanced diagnostic accuracy, predictive planning, and efficient administration automation. With technology developing AI software even smarter, ethics and legislation have to follow in order to allow responsible AI integration. To make AI in dental care work at its best, future research will have to prioritize AI interpretability, developing uniform protocols, and collaboration between specialties in order to allow AI’s full potential in dentistry. Full article

Background and Objectives: This study aims to evaluate the diagnostic potential of routinely available hematological parameters for acute myocardial infarction (AMI) by employing an Explainable Neural Network (ENN) model that combines high predictive accuracy with interpretability. Materials and Methods: A publicly available dataset comprising 981 individuals (477 AMI patients and 504 controls) was analyzed. A broad set of hematological features—including white blood cell subtypes, red cell indices, and platelet-based markers—was used to train an ENN model. Bootstrap resampling was applied to enhance model generalizability. The model’s performance was assessed using standard classification metrics such as accuracy, sensitivity, specificity, F1-score, and Matthews Correlation Coefficient (MCC). SHapley Additive exPlanations (SHAP) were employed to provide both global and individualized insights into feature contributions. Results: The study analyzed hematological and biochemical parameters of 981 individuals. The explainable neural network (ENN) model demonstrated excellent diagnostic performance, achieving an accuracy of 94.1%, balanced accuracy of 94.2%, F1-score of 93.9%, and MCC of 0.883. The AUC was 0.96, confirming strong discriminative ability. SHAP-based explainability analyses highlighted neutrophils (NEU), white blood cells (WBC), RDW-CV, basophils (BA), and lymphocytes (LY) as the most influential predictors. Individual- and class-level SHAP evaluations revealed that inflammatory and erythrocyte-related parameters played decisive roles in AMI classification, while distributional analyses showed narrower parameter ranges in healthy individuals and greater heterogeneity among patients. Conclusions: The findings suggest that cost-effective, non-invasive blood parameters can be effectively utilized within interpretable AI frameworks to enhance AMI diagnosis. The integration of ENN with SHAP provides a dual benefit of diagnostic power and transparent rationale, facilitating clinician trust and real-world applicability. This scalable, explainable model offers a clinically viable decision-support tool aligned with the principles of precision medicine and ethical AI. Full article

Drought and excess ambient temperature intensify abiotic and biotic stresses on agriculture, threatening food security and economic stability. The development of climate-resilient crops is crucial for sustainable, efficient farming. This review highlights the role of multi-omics encompassing genomics, transcriptomics, proteomics, metabolomics, and epigenomics in identifying genetic pathways for stress resilience. Advanced phenomics, using drones and hyperspectral imaging, can accelerate breeding programs by enabling high-throughput trait monitoring. Artificial intelligence (AI) and machine learning (ML) enhance these efforts by analyzing large-scale omics and phenotypic data, predicting stress tolerance traits, and optimizing breeding strategies. Additionally, plant-associated microbiomes contribute to stress tolerance and soil health through bioinoculants and synthetic microbial communities. Beyond agriculture, these advancements have broad societal, economic, and educational impacts. Climate-resilient crops can enhance food security, reduce hunger, and support vulnerable regions. AI-driven tools and precision agriculture empower farmers, improving livelihoods and equitable technology access. Educating teachers, students, and future generations fosters awareness and equips them to address climate challenges. Economically, these innovations reduce financial risks, stabilize markets, and promote long-term agricultural sustainability. These cutting-edge approaches can transform agriculture by integrating AI, multi-omics, and advanced phenotyping, ensuring a resilient and sustainable global food system amid climate change. Full article