Chronic non-communicable diseases (NCDs)—including cardiovascular disease, diabetes, chronic obstructive pulmonary disease (COPD), and chronic kidney disease—pose a major 21st-century global public health challenge. They drive high morbidity, mortality, and escalating healthcare costs. Traditional reactive, clinic-centered care models are ill-equipped to meet the ongoing, complex needs of chronic disease patients. This has prompted a shift toward proactive, personalized, and patient-centered approaches. This narrative review examines the transformative potential of emerging digital health technologies (DHTs) in chronic disease prevention and control. It emphasizes the synergistic integration of four key domains: Internet Plus ecosystems, wearable devices and sensors, artificial intelligence (AI) and machine learning, and interactive voice-based follow-up or conversational agents. Internet Plus serves as the foundational infrastructure. It enables seamless data integration, care coordination, telemedicine, and patient empowerment across stakeholders. Wearable devices facilitate continuous, real-time monitoring of physiological and behavioral data, yielding valuable insights for timely interventions in cardiovascular, metabolic, respiratory, and musculoskeletal disorders. AI and machine learning drive predictive diagnostics, risk stratification, and personalized digital therapeutics, demonstrating superior efficacy and cost-effectiveness in areas like pulmonary rehabilitation and orthopedic care. Voice-based technologies provide scalable, low-cost solutions for medication adherence, symptom monitoring, and health education. They particularly benefit older adults and rural populations. Despite these advances, significant challenges remain. These include data security and privacy risks, health inequities amplified by the digital divide and device biases, and AI limitations (e.g., reproducibility, opacity or “black-box” issues, and unclear legal accountability). In conclusion, the convergence of these technologies promises a more precise, proactive, and inclusive paradigm for chronic disease management. Future success hinges on robust privacy protections, inclusive design, diverse real-world validation, and refined regulatory frameworks to ensure equitable and sustainable implementation.

The Reimagining Organic Chemistry Laboratories in Education Systems (ROLES) initiative at Spelman College was established to provide undergraduate students with synthetic, analytical, computing, and critical-thinking skills necessary for training innovators and problem solvers for future STEM careers. Embedded in the second-semester organic chemistry laboratory, the initiative revamps the course to take on a research-based structure that incorporates purposeful laboratory activities that illustrate the alignment of chemical concepts introduced in teaching laboratories with techniques utilized in the chemical workforce. The ROLES research-based instruction features student-relatable modules of drug discovery, polymer-cosmetic chemistry, and polymer-material science all with real-world applications. Each module is accompanied by computational approaches including machine learning in Artificial Intelligence (AI), electronic Density Functional Theory (DFT) calculations, and docking molecular-modeling studies. The laboratories also utilize instrumentation including NMR, FTIR, GC, and MS spectroscopies. In all, the ROLES initiative provides a transformative blueprint through the chemistry curriculum to actively engage students and deliver connections between chemical theory and everyday life. The intent of this perspective is to describe how real-life applications and the use of modern technology, and instrumentation can be folded into an introductory chemistry lab. This perspective will focus on the logistics of the new curriculum, computational methodologies used, and how spectral analysis is applied throughout the program.

This study aims to thoroughly investigate the perceptions, self-perceived readiness, and existing attitudes of primary education teachers towards the integration of Artificial Intelligence (AI) into their pedagogical practice, recognizing their crucial contribution. Conducted with a quantitative methodology on a sample of five hundred teachers, the study reveals a moderate level of readiness, with only 25% expressing a high degree of confidence. Insufficient professional training and limited access to relevant tools and infrastructure emerge as major challenges, constituting systemic obstacles. While teachers acknowledge the potential benefits of AI for learning, they simultaneously express significant concerns regarding its impact. The findings underscore the urgent need for strategic planning and the implementation of targeted professional development programs. These must encompass not only the necessary technical proficiency but also the essential pedagogical and ethical dimensions of AI, shaping a cohesive supportive framework for the responsible and equitable utilization of AI in the education of the future.

The integration of Artificial Intelligence (AI) in education holds transformative potential, particularly in advancing Education for Sustainable Development (ESD) and green energy literacy. However, empirical evidence is lacking on how teacher involvement in ESD material development influences their capacity to integrate AI into pedagogical contexts. This study aims to examine the relationships among AI knowledge, attitudes toward AI, involvement in material development, and teachers' ability to apply AI in green energy education. Employing a quantitative correlational design, data were collected from 122 in-service teachers engaged in professional development programs. A structured questionnaire measured five constructs, which were analysed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The results indicated that teachers' practical use of AI in science and green energy learning (β = 0.391, p < 0.01, f2 = 0.180) and their involvement in developing ESD-based teaching materials (β = 0.236, p < 0.05, f2 = 0.127) significantly influenced their AI integration capabilities. In contrast, AI knowledge and attitudes toward AI did not show statistically significant effects. These findings suggest that experiential engagement with AI and active participation in sustainability-focused content development are stronger predictors of AI pedagogical readiness than cognitive or affective factors alone. This study underscores the importance of shifting teacher training toward context-based, practice-oriented models to enhance technological competence in sustainability education.

Problem statement The invention of artificial intelligence (AI) has transformed learning, causing the emergence of different learning patterns which blended learning is part of. This underscores why this study explores undergraduates’ perceptions and anxieties on AI integration into blended learning. Method The population of this study is 1523 undergraduates of Kwara State University, Malete, Nigeria. Krejcie and Morgan Table was used to select 371 respondents who are chosen randomly. A self-developed questionnaire was used for data collection. The Cronbach Alpha reliability of the questionnaire was 0.72. Data collected was downloaded in csv format and analysed with SPSS, using frequencies, percentages and mean ( X ̅). Results Findings revealed that the respondents are apprehensive because the use of AI for learning will cause changes in teaching ( X ̅ = 2.94), lack of control on the use of AI for learning ( X ̅ = 2.85) and use of AI for learning will cause overdependence on it ( X ̅ = 2.80). Furthermore, the respondents perceived that AI is powerful for solving complex learning problems ( X ̅ = 334), enable learning solutions ( X ̅ = 3.34), enhance learning outcomes ( X ̅ = 3.32), personalise learning experience ( X ̅ = 3.32), strengthen digital literacy skills ( X ̅ = 3.27) and make learning efficient ( X ̅ = 3.25). Moreso, AI makes the respondents learn remotely ( X ̅ = 3.37), creates flexible pathways for learning ( X ̅ = 3.30) and allows access to information about their courses ( X ̅ = 3.26). However, poor Internet bandwidth ( X ̅ = 3.40), inadequate funding of the university to acquire AI infrastructure needed for blended learning ( X ̅ = 3.06), lecturers resisted using AI for learning enhancement ( X ̅ = 2.85) and doubt of the quality and accuracy of AI-generated contents ( X ̅ = 2.82) affect the integration of AI into blended learning. Conclusion This study concludes that undergraduates of LIS have different perceptions and anxieties about AI.

Background: Integrated medical information systems process large volumes of sensitive clinical data and are exposed to persistent cyber threats. Artificial intelligence (AI) is increasingly used for anomaly detection and incident response, yet its systemic effect on the dynamics of security indicators is not fully quantified. Aim: To develop and numerically study a nonlinear dynamical model describing the joint evolution of system vulnerability, threat activity, compromise level, AI detection quality, and response resources in a medical data protection context. Method: A five-dimensional system of ordinary differential equations was formulated for variables V, T, C, D, R. Parameters characterize appearance and elimination of vulnerabilities, attack intensity, AI learning and degradation, and resource consumption. The corresponding Cauchy problem V0=0.5, T0=0.6, C0=0.1, D0=0.4, R0=0.8 was solved on 0,200 numerically using a fourth-order Runge–Kutta method. Results: Numerical modelling showed convergence to a favourable steady regime. On the interval t ∈ [195, 200] the mean values were V=0.0073, T=0.3044, C=7.7·10−5, D=0.575, R=19.99. Thus, the initial 10% compromise is reduced by more than 99.9%, while AI detection quality stabilizes at around 0.58, and response capacity increases 25-fold. Conclusions: The model quantitatively confirms that the integration of AI detection and a managed response capacity enables the system to reach a stable state with virtually zero compromised medical data even with non-zero threat activity.

This research has successfully addressed the challenges attributed with SMS, including the fragmented data, heavy reliance on experience, and lack of life cycle integration. This study presents the development and validation of a novel sustainable material selection (SMS) model using Artificial Intelligence (AI). The proposed model structures the process around four core life cycle phases—design, construction, operation and maintenance, and end of life—and incorporates a dual-interface system. This includes a main credits interface for high-level tracking of 100 total credits to trace the dynamics of SMS in relation to energy efficiency, indoor air quality, site selection, and efficient use of water. Further, it includes a detailed credit interface for granular assessment of specific material properties. A key innovation is the formalization of closed-loop feedback mechanisms between phases, ensuring that practical insights from construction and operation inform earlier design choices. The model’s functionality is demonstrated through a proof of concept for SMS considering thermal properties, showcasing its ability to contextualize benchmarks by climate, map properties to building components via a weighted networking system, and rank materials using a comprehensive database sourced from the academic literature. Automated scoring aligns with green building certification tiers, with an integrated alert system flagging suboptimal performance. The proposed model was validated through a structured practitioner survey, and the collected responses were analysed using descriptive and inferential statistical analysis. The result presents a scalable quantitative AI-assisted decision-making support model for optimizing material selection across different project phases. This work paves the way for further research with additional assessment criteria and better integration of AI and Machine Learning for SMS.

Robotic surgery has emerged as a key minimally invasive approach for gastrointestinal malignancies, stimulating substantial global research activity. This study employed bibliometric and visual methods to map the knowledge structure, evolutionary trajectory, research hotspots, and emerging trends in this field. We systematically retrieved relevant publications in this field from the Web of Science Core Collection over the past decade and conducted a visualization analysis. The findings delineate four major research hotspots in this field, including comparative effectiveness research against laparoscopy, technical refinement and standardization, perioperative outcome optimization, and the integration of artificial intelligence (AI) and deep learning. The field's focus has evolved from initial feasibility studies toward recent investigations involving AI, deep learning, risk prediction, enhanced recovery after surgery, and multidisciplinary integration. The comprehensive integration of AI and deep learning, particularly through predictive modeling and intraoperative navigation, represents a key direction for future research. This study provides valuable guidance and insights for shaping future research agendas and refining clinical practice in this rapidly advancing field.

AI-driven aging digital twins: A roadmap for clinical translation in precision geriatrics.

Influenza's rapid evolution, driven by its segmented RNA genome, high mutation rate, and extensive animal reservoirs, underpins its capacity to cause recurring epidemics and unpredictable pandemics. Recent advances in artificial intelligence (AI) and machine learning (ML) are transforming influenza forecasting by enabling the prediction of viral evolution and the optimisation of public health preparedness. This review synthesises insights from historical data (1890-2025) and contemporary research to examine the evolving role of AI in influenza prediction. It highlights major developments including transformer-based models for viral evolution, real-time integration of mobility and environmental data, hybrid quantum, which are classical algorithms, and multimodal data fusion frameworks, it also consideres critical risk modifiers such as meteorological variation, armed conflict, and host genetics. Importantly, the review distinguishes between retrospective, proof-of-concept analyses and prospective, real-time forecasting applications, clarifying their respective contributions to operational public health preparedness and informed decision-making.

Residual disease in NPM1-mutated acute myeloid leukemia.

Radiographic Data Segmentation as a Tool in Machine Learning and Deep Learning Artificial Intelligence Algorithms

Assessing deep learning artificial intelligence support for detecting elbow fractures in the pediatric emergency department.

This study aims to explore and optimize the "teaching, practicing, and competing" sports teaching model by integrating artificial intelligence (AI) technology. It focuses on building a new intelligent ecosystem for sports education, emphasizing the connection between "teaching," the bridging role of "practicing," and the efficiency of "regular competition" to enhance students' sports skills and literacy. Through AI, the study enables personalized teaching content, intelligent practice feedback, and automated competition management. This approach provides innovative ideas and theoretical support for school physical education, promotes the deep integration of "learning, practicing, and competing," and strengthens school sports education in the new era. This paper explores the new path of AI-enabled extracurricular physical activity practice from the perspective of "teaching, practicing, and competing" by means of literature and logical analysis to lay a theoretical foundation for promoting the development of school physical education. Currently, there is a formalism erosion in extracurricular activities in schools, single and boring organizations, and a lack of activity resources, which seriously impede the integrated development of "learning, training, and competing" and affect students' interest and the effect of physical exercise. The AI intelligent sports program in this study integrates artificial intelligence and competitive elements, which can greatly enhance students' motivation to learn and practice. At the same time, the system can realize the daily exercise and competition, flexibly arrange the study and practice plan, combine online and offline, enrich the after-school life, and cultivate sports habits. Secondly, the AI system provides comprehensive data support to ensure fair and accurate teaching evaluation. Finally, AI Smart Sports also saves teaching resources, reduces teachers' burden, and improves teaching efficiency and quality through intelligent teaching assistance. Since the AI intelligent sports system is in the initial development stage, there are still deficiencies, so in order to better solve the dilemma of the problem put forward the following suggestions: (I) Innovate the content of AI learning and practicing, the core of the AI intelligent sports system lies in the diversified learning and practicing content, and the need to continue to update sports knowledge. (II) Build an information-sharing platform to promote cross-school communication, integrate teaching resources, and stimulate dynamic updating potential. (III) Implement stratified optimization management, staggered use of resources, and intelligent scheduling to improve efficiency. (IV) Establish a quality assessment system, collect user feedback, regularly evaluate system effectiveness, and continuously optimize content to ensure user experience.

This research examines teachers' perceptions of the use of artificial intelligence (AI) in daily learning at elementary schools. The development of AI technology has brought significant transformation to the world of education, offering great potential to improve the quality of learning through personalized materials, automation of administrative tasks, and provision of interactive learning aids. However, the implementation of AI in elementary school learning still faces various challenges related to teachers' perceptions, competencies, and readiness. The research objectives are to analyze teachers' understanding of AI in the learning context, explore teachers' experiences in using AI-based applications, identify the benefits and challenges faced, evaluate the effectiveness of AI in supporting differentiated learning, and identify support needs for optimizing AI utilization. This research uses a qualitative approach with descriptive methods. Data were collected through in-depth interviews with six classroom teachers (grades 1-6), one school principal, and one school operator at SDN Rinjani, Cirebon City, in November-December 2024. The research instrument was a semi-structured interview guide. Data analysis used the Miles and Huberman model through stages of data reduction, data presentation, and conclusion drawing. Data validity was ensured through source triangulation and member checking. The research findings show that teachers have diverse understandings of AI, ranging from basic concepts as assistive tools to more comprehensive understanding of AI's role in learning transformation. The AI applications used include PowerPoint, Wordwall, Assemblr Edu, ChatGPT, Gemini, Perplexity, and Canva. Teachers reported various benefits of AI such as ease of access to information, efficiency in creating materials, personalized learning, and increased student creativity. The challenges faced include limited teachers' digital competence, suboptimal technological infrastructure, and the risk of excessive dependence on AI. AI was found to be effective in supporting differentiated learning through adaptation of content, process, and learning products according to individual student needs. The required support includes systematic training, infrastructure improvement, and supportive policy development. The research concludes that although teachers show positive attitudes toward AI, optimal implementation requires comprehensive investment in developing teacher competence and technological infrastructure. Further research is recommended to examine the long-term impact of AI use on student learning outcomes and develop effective teacher training models.

Lung cancer is a leading cause of cancer-related deaths worldwide. Artificial intelligence (AI) has emerged as a transformative force in modern healthcare, revolutionizing diagnostic accuracy, treatment efficacy, and patient management. This review aims to investigate the current state of AI and machine learning applications in lung cancer care, focusing on critical studies, achievements, and future directions. AI technologies, such as machine learning and deep learning, have shown promise in improving lung cancer diagnosis, predicting treatment outcomes, and enhancing patient care. The benefits of AI in healthcare, including personalized recommendations, improved diagnostic accuracy, and enhanced patient outcomes. Also explore the applications of AI in lung cancer, including image analysis, risk prediction, and treatment planning.

Introduction. Inborn errors of immunity (IEI), also known as primary immunodeficiencies, comprise a heterogeneous group of rare genetic disorders with a wide range of clinical manifestations, including increased susceptibility to infections, autoimmune and inflammatory conditions, allergic diseases, and malignancies. Diagnosing these conditions is often challenging due to the nonspecific nature of symptoms, limited access to molecular genetic testing, and low clinical awareness among healthcare providers. In this context, the potential of artificial intelligence (AI) to improve the diagnosis of IEI is gaining increasing attention. The aim of this review is to analyze current and potential applications of AI in the diagnosis of IEI and to discuss the challenges of implementing such technologies in Ukrainian clinical practice. Materials and Methods. A comprehensive analysis of scientific literature was conducted, focusing on the use of AI and machine learning (ML) in the diagnosis of IEI and data-driven clinical decision-making. Sources were searched using databases such as PubMed, Scopus, and Web of Science. Results. The article outlines the primary areas of AI application in IEI diagnostics, including automated processing of clinical data, analysis of medical images, interpretation of next-generation sequencing (NGS) data, predictive modeling, and development of electronic decision-support systems. AI has been shown to reduce the time to diagnosis, decrease the number of unnecessary tests, standardize clinical approaches, and enhance access to personalized medicine. Current and emerging directions of AI use in IEI diagnosis are considered in light of ethical, technological, and practical aspects. Several successful cases of AI implementation in IEI diagnostics and clinical decision-making are presented. Key barriers to AI implementation in Ukraine are also highlighted, including insufficient digitalization of healthcare institutions, data fragmentation, lack of anonymized clinical datasets, ethical and legal concerns, and the need for interdisciplinary training of specialists. Conclusions. Artificial intelligence is a promising tool in the diagnosis of IEI, offering the potential for more accurate, timely, and cost-effective identification of these rare conditions. Its effective use requires a national digital health strategy, international collaboration, and the development of localized tools tailored to the Ukrainian healthcare context.

The use of Artificial Intelligence (AI) for teachers to support digital literacy in learning is an increasingly relevant issue in the context of modern education. This community service project outlines the main issue by explaining the challenges and potential of AI in learning and helping teachers improve their individual professionalism. The community service method used was a literature review, which included an analysis of various sources and views related to the use of AI in secondary education. This was conducted by going directly to the field to find out the extent of the problem. The results showed that many teachers still do not understand the use of Artificial Intelligence (AI) as a practical tool that makes things easier within their limited capabilities. It was found that some teachers already understand how technology can speed up work and simplify administrative work, but others still need to improve their skills. In conclusion, teachers' understanding of the role of AI in the digital transformation of education is still at an early stage, technical and pragmatic.

The rapid integration of Artificial Intelligence (AI) tools into creative and commercial industries necessitates a corresponding shift in higher education curricula. This paper presents a classroom-based case study investigating engineering students' awareness and perception of AI integration within a "Packaging Design and Technology" course at a technical university. The project required students to design a sustainable chip package and execute a TikTok marketing campaign, explicitly mandating the use of AI tools across the entire workflow; from ideation and graphic design to content creation and campaign strategy. Through surveys, analysis of the project brief, AI workflow examples, and a review of contemporary literature, this study establishes a framework for evaluating student AI literacy, ethical awareness, and perceived impact on creativity and efficiency. Preliminary findings suggest that structured, mandatory integration of AI in project-based learning is a critical pedagogical approach for bridging the skills gap between academic training and industry demands in the converging fields of design, engineering, and marketing.

This paper presents a grounded theory study involving 20 older teacher educators (over the age of 55) who discussed their approaches to learning processes in the context of artificial intelligence (AI) technology. The data collection process consisted of three semi-structured interviews with each participant, supplemented by written reflections, email correspondence, and observations. Professional identity negotiation through technological adaptation was the central category describing the transformation from opposition to integration, as determined by systematic three-stage coding procedures: open, axial, and selective coding, along with constant comparison and theoretical sampling. The results indicate that early resistance in older teachers serves as a professional protective measure against identity threats to the validity of their expertise, pedagogical independence, and value congruence. It was integrated by means of strategic action/interaction strategies such as peer learning, pedagogical reframing, selective appropriation, and taking up mentorship roles, which were mediated by contextual conditions (infrastructure of institutional support, stage of career, previous technology experiences) and intervening conditions (ageist discourses, ethical controversy, and disciplinary norms). The consequences were differentiated into selective integration with recon15structed professional identities and persistent consequences with the marginalisation of professionals. Substantive theory confronts deficit discourses, which seek to frame resistance as a failure on the part of the individual. However, it presents identity negotiation as a complex social process that requires supportive conditions in institutional and learning infrastructure, maintenance of autonomy, and legitimisation of selective integration as advanced professional practice.