The integration of robotics and artificial intelligence (AI) into nanomedicine represents a significant advancement in developing targeted therapeutic and diagnostic platforms. This field focuses on engineering micro- and nanoscale agents, such as magnetic nanoparticles (MNPs), microbots, and nanobots, for tasks like targeted therapies, sensing, and manipulation at diseased sites. MNPs are typically composed of iron oxides and serve as foundational components due to their biocompatibility, tunable surface chemistry, and responsiveness to external magnetic fields. They are used in targeted drug delivery, magnetic hyperthermia for tumor ablation, and as contrast agents in magnetic resonance imaging (MRI) and magnetic particle imaging (MPI). Microbots and nanobots, which often incorporate MNPs for propulsion, can be actively guided using external magnetic fields to navigate complex biological environments, perform micromanipulation, and enable triggered drug release. The precise control of these magnetic agents relies on electromagnetic or permanent magnet-based guidance systems, which balance magnetic force strength, workspace volume, and clinical integration. Other classes like biohybrid microbots or DNA nanobots, utilize magnetic field independent mechanisms for molecular sensing and cargo delivery. AI and machine learning enhance these systems by optimizing material and bot design through in silico modeling, facilitating real-time navigation via medical imaging feedback, and enabling adaptive pathfinding. AI can also support swarm control and data analysis for diagnostic improvement. However, clinical translation faces challenges, including ensuring long-term biocompatibility and biodistribution, achieving scalable Good Manufacturing Practice (GMP) production, demonstrating therapeutic advantage in preclinical models, navigating evolving regulatory frameworks, and securing sufficient funding.

When distress meets technology: The mediating role of AI integration in the link between nurses' moral distress and moral integrity in critical care settings.

The value of artificial intelligence combined with multimodal data analysis in tumor immunotherapy and targeted therapy.

Coronary artery calcium clinical utilization: An update.

Integrating artificial intelligence (AI) into drug discovery revolutionizes pharmaceutical research by significantly accelerating the identification, optimization, and development of novel therapeutics. Conventional drug discovery methods, known for high costs, lengthy timelines, and low success rates, are increasingly being augmented by AI-based technologies, including machine learning (ML), deep learning (DL), and reinforcement learning (RL). These advanced computational approaches enhance key processes, such as target identification, virtual screening, de novo drug design, toxicity prediction, and the optimization of pharmacokinetic and pharmacodynamic profiles, dramatically increasing overall efficiency. AI-driven primary and secondary screening methods improve cell classification, compound prioritization, and drug-target interaction predictions, substantially shortening the progression from preclinical phases to clinical trials. Additionally, AI enables retrosynthesis prediction and reaction yield modeling, optimizing chemical synthesis pathways and reducing the need for resource-intensive experimental procedures. AI's integration into clinical trials has notably improved patient stratification, biomarker discovery, and adaptive trial designs, ultimately delivering more precise and economically feasible therapeutic interventions. Furthermore, AI supports polypharmacological approaches through multitarget drug discovery, drug repurposing (finding new uses for existing drugs), and adverse effect prediction, thereby advancing personalized medicine. Despite these transformative advantages, it's important to note that AI in drug discovery also has limitations, such as ensuring data quality, improving model interpretability, gaining regulatory acceptance, and addressing ethical concerns. This review comprehensively explores the impact of AI throughout the drug discovery pipeline, emphasizing its critical role in expediting the development of life-saving medications and outlining future directions for continued pharmaceutical innovation driven by AI.

The digital twin (DT) concept, originating from engineering disciplines, has emerged as a transformative technology in healthcare, particularly in oncology. A digital twin creates a dynamic, virtual replica of a patient's physiological and pathological state, integrating multi-dimensional data to enable personalised cancer care. Despite growing interest, comprehensive reviews examining the breadth of DT applications in oncology remain limited. This narrative review aims to synthesise current evidence on digital twin applications in oncology, evaluate their potential to transform cancer care delivery, and identify challenges hindering clinical translation. A comprehensive literature search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases from inception to September 2025. Studies describing DT development, validation, or application in any cancer type were included. Grey literature, conference proceedings, and expert commentaries were also reviewed to capture emerging trends. Digital twins demonstrate applications across the cancer care continuum, including precision treatment selection, radiotherapy optimisation, drug development, immuno-oncology modelling, surgical planning, and survivorship care. Integration of multi-omics data, imaging biomarkers, and artificial intelligence enables dynamic simulation of tumour behaviour and treatment response. However, challenges persist in data integration, model validation, computational scalability, and ethical governance. Digital twin technology holds substantial promise for advancing precision oncology through predictive, personalised, and adaptive care strategies. Addressing current limitations through interdisciplinary collaboration and regulatory framework development is essential for clinical implementation.

AI-Driven nanofiber platforms for essential oil delivery in dry period cows: a sustainable strategy against mastitis and antimicrobial resistance.

Beyond binary diagnosis: Key questions on AI accuracy, real-world applicability, and safety in clinical decision support.

A comprehensive review of emerging protein modification methods: modified properties and potential applications.

The complementary integration of artificial intelligence (AI) in the workplace requires balancing performance goals with psychological needs, as both are essential for sustained outcomes. This study examines different workflows (AI-first and human-first) as cognitive forcing strategies to test whether they enhance performance and psychological outcomes compared to human-only and AI-only processing. In a one-factorial between-subjects experiment (N = 101) within a visual inspection task, evaluated at up to three measurement points, performance variables (accuracy, speed, error rates) and psychological variables (vigilance, flow, teaming experience, wellbeing when working with the AI) were assessed. Human-AI collaboration outperformed AI-only in error rates (η2=0.29) and human-only in speed (η2=0.11 - 0.14), but only when AI preceded human processing. The AI-first workflow enhanced teaming perception compared to human-only processing (η2=0.07). Moreover, human-AI collaborative processing reduced flow decrease compared to human-only processing (η2=0.07). Overall, AI processing preceding human processing produces the best balance between performance and psychological outcomes in safety-critical inspection tasks, supporting a holistic view of AI integration in the workplace.

Artificial intelligence (AI) is increasingly embedded in language education, and learners' acceptance of AI, together with their multilingual learning enjoyment (MLE) and self-efficacy (SE), is considered pivotal to meaningful learning gains. This study developed an AI Acceptance (AIAC) framework tailored to multilingual language learning and examined whether MLE mediates the association between AIAC and SE. A total of 524 multilingual undergraduates from 11 Chinese universities participated. The scale was constructed and validated through Exploratory Factor Analysis (EFA) with 235 participants and Confirmatory Factor Analysis (CFA) with 289 participants to evaluate model fit and refine items. The finalized framework comprises 14 items across four dimensions - Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Behavioral Intention to Use (BI), and Actual Usage (AU) - capturing learners' AIAC in multilingual tasks. Furthermore, Structural Equation Modeling (SEM) was used to examine how AIAC is associated with learners' SE, focusing on the mediating role of MLE. Results showed both direct and indirect associations between AIAC and SE, with MLE statistically operating as a partial mediator. Based on these findings, the study recommends integrating AI into supportive multilingual tasks and immersive intercultural activities, together with AI value-informed guidance and practical learning support, to help foster acceptance, promote enjoyment, and support learners' SE in multilingual contexts. The validated AIAC framework offers a concise, context-sensitive instrument for research and program evaluation in multilingual settings and provides actionable guidance for curriculum design and teacher development aimed at sustainable AI integration.

The rapid expansion of Artificial Intelligence (AI) in educational settings has transformed writing practices, assessment structures, patterns of student engagement, and underlying epistemological assumptions about knowledge production. While AI systems offer unprecedented opportunities for cognitive scaffolding, emotional regulation, and inclusive participation, they simultaneously challenge traditional notions of authorship, intellectual agency, and pedagogical authority. The integration of AI into school practice therefore requires robust theoretical grounding, ethical governance, and organizational coherence. This article develops a comprehensive, human centered framework that connects AI literacy, critical digital literacy, collaborative digital writing, metacognition, emotional intelligence, and organizational culture in secondary education. Drawing upon interdisciplinary research in areas such as AI and adolescent emotional well being, AI and school related anxiety, collaborative ICT based inclusion, digital tools as cognitive instruments, technology as cultural bridge building practice, organizational culture and school vision, metacognition and emotional intelligence models, theory of mind in ICT contexts, digitally assisted mindfulness, and psychoanalytic cultural theory, the study proposes a multilayered instructional model for AI supported collaborative digital writing. The article argues that AI literacy must be cultivated not merely as technical competence but as epistemic responsibility embedded within reflective, relational, and culturally coherent school ecosystems. Collaborative digital writing emerges as a pedagogically optimal environment for fostering metacognitive regulation, socio emotional awareness, critical evaluation, and inclusive participation. The study concludes that AI integration in education must be guided by visionary leadership, organizational culture, ethical transparency, and human centered pedagogical design.

Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most prevalent chronic liver condition globally, shifting the diagnostic paradigm toward an affirmative, metabolism-focused framework. The gut-liver axis is a central pathophysiological pathway. This review aims to synthesize revolutionary advances from 2023 to 2025 in understanding and treating MASLD by focusing on the gut microbiome's role. This comprehensive review analyzes cutting-edge research published between 2023 and 2025. We examined evidence from landmark clinical trials, developments in next-generation probiotics, the integration of artificial intelligence (AI) with multiomics for diagnostics, and studies clarifying the interplay between host genetics and the microbiome in MASLD pathogenesis. Causal links between gut dysbiosis and MASLD pathology are now firmly established. Fecal microbiota transplantation (FMT) effectively prevents hepatic encephalopathy recurrence, and next-generation probiotics like Akkermansia muciniphila have entered MASLD-specific trials. AI-driven diagnostic tools have achieved regulatory qualification from the European Medicines Agency. Furthermore, host genetics, particularly PNPLA3 variants, are shown to not only predispose to MASLD but also shape specific microbial communities that functionally contribute to disease progression. The field is rapidly advancing from correlative observations to causal evidence, enabling the development of microbiome-based biomarkers and personalized therapies. The future of MASLD management lies in precision strategies, such as bacteriophage therapy and functionally defined probiotics, which integrate metabolic, microbial, and genetic factors into individualized care, heralding a new therapeutic era.

The rapid development of digital technologies and the emergence of the digital economy have significantly transformed managerial decision-making processes. Digitalization affects all areas of enterprise activity, including data collection, processing, and analysis, which directly influences the speed, quality, and effectiveness of managerial decisions. Modern managers increasingly rely on digital tools, analytical platforms, decision support systems, artificial intelligence (AI), and large datasets, enabling more informed, timely, and flexible decision-making. These capabilities are essential for maintaining competitiveness in dynamic and uncertain business environments. This article analyzes the transformational features of managerial decision-making under digitalization, emphasizing theoretical foundations and practical applications. Effective managerial decisions are determined not only by their formulation but also by successful implementation, which ensures achievement of both strategic and operational objectives. Key contemporary aspects include digitalization and automation, real-time data-driven analysis, scenario modeling, AI integration, flexibility, and adaptability. These features enhance optimization, responsiveness, and overall managerial efficiency, particularly in enterprises such as bakeries, where production, financial, marketing, and personnel management must respond to fluctuating markets, regulatory changes, and consumer expectations.The classification of managerial decisions is considered with respect to management levels, problem complexity, personnel involvement, functional orientation, and decision-making conditions. Digital technologies facilitate process automation, data integration, collective decision-making, and transparency, enabling managers to make well-grounded, efficient, and adaptive choices. The study demonstrates that incorporating digital tools and AI in decision-making improves performance, strengthens competitiveness, and supports sustainable development of modern enterprises.

This study examines the impact of artificial intelligence (AI) on the sustainability of small to medium-sized tourism enterprises (SMTEs) through an extensive bibliometric analysis of research conducted between 2014 and 2025. The goal is to identify prevailing trends, challenges, and opportunities related to AI integration in the tourism sector, especially concerning SMTEs. Given the rapid advancements in AI-driven technologies, the study also highlights the increasing role of generative AI models in content creation to enhance customer interactions and emphasizes the need for designing and evaluating AI tools tailored to address the constraints faced by SMTEs. A bibliometric study was conducted using the Scopus database, narrowing an initial set of 643 records down to 373 relevant articles. VOS Viewer software (v1.6.20) was used to map publication trends, country contributions, journal outlets, author collaborations, and thematic clusters. To provide a comprehensive overview of the field, the study combined network analysis and thematic analysis. The results indicate a steady increase in publications over the study period, with a notable acceleration after around 2019. International collaboration is also growing, reflecting increased global interest in AI and sustainable tourism. However, despite this growth, only a small portion of studies explicitly focus on SMTEs; most research either addresses larger tourism enterprises or applies generalized models that do not account for SMTE-specific constraints and contexts. The study emphasizes the need for ongoing research to ensure AI tools are used in ways that foster innovation and sustainability within specific types or niche SMTEs, while developing and evaluating AI tools that are affordable, scalable, and responsive to their constraints.

This review explores the integration of artificial intelligence (AI) in mobile health applications for diabetes care. It focuses on key AI methodologies - machine learning, deep learning, and natural language processing - and their roles in glucose monitoring, personalized self-management, risk prediction, and clinical decision support. Drawing on recent literature (2018-2024), the study outlines the benefits of AI in improving accuracy, engagement, and precision in diabetes treatment. Challenges such as data privacy, algorithmic bias, and regulatory barriers are also examined. A new section discusses when AI technologies may become burdensome, especially in low-resource settings or for users with limited digital literacy. The review concludes with directions for enhancing model explainability and integrating AI with wearable and Internet of Things devices, emphasizing the need for ethical and equitable implementation in future diabetes management strategies.

This study presents a thorough analysis of the Artificial Intelligence (AI) and Human-Computer Interaction (HCI) intersection, with the aim of identifying important trends, themes, and influential research in this rapidly changing field. The integration of AI into HCI has resulted in significant advancements across various domains, such as healthcare, education, and user experience design. Although there is a growing interest in this area, the number of studies is still limited, and the research is gradually increasing. This study aims to fill the gap by providing a comprehensive overview of the current literature, focusing on the gaps, emerging trends, and future directions in AI-driven HCI. The research methodology adheres to the PRISMA protocol, which guarantees a systematic and clear review process. A total of 84 peer-reviewed publications from the Scopus database, spanning a 30-year period from 1994 to 2025, were examined. The research corpus was subjected to bibliometric analysis, Social Network Analysis (SNA), and text mining techniques to map the landscape of AI and HCI research. The study also recognized key authors, influential countries, and significant academic sources contributing to this field. The results of the analysis identified five primary thematic groups: Explainable AI (XAI), Human-Computer Interaction (HCI) and AI in Education and Training, Human-Robot Interaction (HRI), and AI and User Experience (UX). These themes emphasize the wide-ranging applications of AI in HCI, such as enhancing diagnostic precision in healthcare, personalizing educational content, and enhancing user experience through adaptive and emotionally intelligent interfaces. However, the study also revealed significant gaps in the existing literature, particularly regarding ethical considerations, transparency, and user control. The analysis indicates that ethical issues are not adequately emphasized in current research, suggesting a crucial area for future investigation. The study suggests that while AI has considerable potential to transform HCI, its successful incorporation will depend on addressing these gaps and ensuring that AI-driven systems prioritize human-centered design principles. The results also highlight the prominent role of countries like the People's Republic of China (PRC) in advancing this field, and emphasize the need for broader international cooperation. This research provides a deeper understanding of the evolving landscape of AI and HCI and serves as a foundation for future studies.

The integration of Artificial Intelligence (AI) in marketing automation has transformed customer engagement, data-driven personalization, and campaign optimization across industries. However, the opaque nature of AI decision-making raises concerns about transparency, trust, and ethical compliance, particularly in sensitive domains such as healthcare and finance. This review explores the design of Explainable AI (XAI)-based marketing automation architectures that prioritize interpretability, fairness, and regulatory alignment. It examines how explainability frameworks—such as SHAP, LIME, and counterfactual reasoning—can enhance model transparency without compromising predictive accuracy. The paper compares architectural strategies for embedding XAI within Customer Relationship Management (CRM), lead scoring, and content personalization systems in healthcare and financial institutions. By analyzing recent advancements in hybrid explainability models, knowledge graphs, and AI auditing pipelines, this review highlights how organizations can achieve responsible automation while meeting sector-specific compliance standards like HIPAA, GDPR, and Basel III. The study concludes by outlining a reference architecture for XAI-driven marketing automation that balances algorithmic interpretability with business performance, supporting ethical personalization, trustful decision-making, and sustainable digital transformation across regulated industries.

ABSTRACT This systematic review analyses 49 empirical studies (2010–2025) examining how educational leadership supports teachers’ psychological needs – autonomy, competence, and relatedness – during the integration of artificial intelligence (AI) in K-12 schools. Guided by the Unified Model of Effective Leadership Practices, the analysis highlights emphasis on professional development and limited attention to vision building and partnership development. A sixth domain, institutionalizing AI audit mechanisms, emerges, emphasizing institutional vigilance and relational trust. The review extends leadership scholarship by framing effective AI leadership as relational and psychologically attuned, and by advocating a balanced investment in technological advancement and teachers’ agency for sustainable innovation.

A clinical-AI correlation for integrating artificial intelligence into stroke care: a systematized literature review and practice framework.