Search Results (844)

Trust in public institutions was challenged during the COVID-19 global pandemic, with widespread mistrust towards healthcare institutions as well as fellow public institutions. Concurrently, a new public institution or social tool, mass-market artificial intelligence (AI), more broadly emerged, which too may be a target of fluctuating public trust. Using national survey data from the United Kingdom’s Centre for Data Ethics and Innovation (survey year: 2022, N = 4320; survey year: 2023, N = 4232), we explore the level of trust in civic institutions (healthcare, non-healthcare, and AI) during and immediately after the COVID-19 pandemic in the United Kingdom using a naturalistic quasi-experimental design. At both waves (2022 and 2023), principal component analysis and structural equation modeling over thirteen public institutions and AI variables confirmed three factors (or domains) of public trust: trust in healthcare institutions, trust in fellow civic institutions other than healthcare, and trust in AI. Measurement invariance testing of mean levels of public trust along each distinct component revealed that as compared with 2022, in 2023, (1) trust in healthcare institutions and in fellow civic institutions other than healthcare significantly increased and (2) trust in AI remained approximately level. Next, latent profile modeling revealed four levels of a common public trust profile, with all three domains of public trust being normatively closely associated. Taken together, these results suggest that a psychological stance of public trust, PT, may increase after a societal crisis. Full article

The increased importance of sustainability imperatives has required a profound reconsideration of the interaction between materials, manufacturing, and design fields. Biomimetic smart materials such as shape-memory polymers, hydrogels, and electro-active composites represent an opportunity to combine adaptability, responsiveness, and ecological intelligence in systems and products. This work reviews the confluence of such materials with leading-edge manufacturing technologies, notably additive and 4D printing, and how their combining opens the door to the realization of time-responsive, low-waste, and user-adaptive design solutions. Through computational modeling and mathematical simulations, the adaptive performance of these materials can be predicted and optimized, supporting functional integration with high precision. On the basis of case studies in regenerative medicine, architecture, wearables, and sustainable product design, this work formulates the possibility of biomimetic strategies in shifting design paradigms away from static towards dynamic, from fixed products to evolvable systems. Major material categories of stimuli-responsive materials are systematically reviewed, existing 4D printing workflows are outlined, and the way temporal design principles are revolutionizing production, interaction, and lifecycle management is discussed. Quantitative advances such as actuation efficiencies exceeding 85%, printing resolution improvements of up to 50 μm, and lifecycle material savings of over 30% are presented where available, to underscore measurable impact. Challenges such as material scalability, process integration, and design education shortages are critically debated. Ethical and cultural implications such as material autonomy, transparency, and cross-cultural design paradigms are also addressed. By identifying existing limitations and proposing a future-proof framework, this work positions itself within the ongoing discussion on regenerative, interdisciplinary design. Ultimately, it contributes to the advancement of sustainable innovation by equipping researchers and practitioners with a set of adaptable tools grounded in biomimicry, computational intelligence, and temporal design thinking. Full article

Organ failure constitutes a significant global concern requiring urgent attention. While organ transplantation offers prospective treatment, it remains suboptimal. The scarcity of donor organs and the need for lifelong immunosuppressive treatments highlight the necessity for innovative approaches in regenerative medicine. In response, tissue engineering has emerged as a promising alternative, particularly through advancements in three-dimensional (3D) and four-dimensional (4D) printing technologies. These approaches enable the fabrication of complex, patient-specific constructs for regenerating tissues such as skin, bone, cartilage, and vascularized organs. This review systematically examines 3D printing techniques, commonly used biomaterials (e.g., hydrogels, bio-inks, and polymers), and their applications in dermal, cardiovascular, bone, and neural regeneration. In addition to discussing 3D technology, an introduction to 4D bioprinting is provided, enabling advanced biomedical applications and establishing itself as an innovative tool that enhances the classic approach to 3D bioprinting in the context of regenerative medicine. Finally, key challenges and ethical considerations are discussed to provide a comprehensive perspective on the current state and future of printed scaffolds in regenerative medicine. 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

Wearable computing is evolving from a passive data collection paradigm into an active, precision-guided health orchestration system. This survey synthesizes developments across sensing modalities, wireless protocols, computational frameworks, and AI-driven analytics that collectively define the state of the art in mental and physical health monitoring. A narrative review methodology is used to map the landscape of hardware innovations—including microfluidic sweat sensing, smart textiles, and textile-embedded biosensing ecosystems—alongside advances in on-device AI acceleration, context-aware multimodal fusion, and privacy-preserving learning frameworks. The analysis highlights a shift toward multiplexed biochemical sensing for real-time metabolic profiling, neuromorphic and analog AI processors for ultra–low-power analytics, and closed-loop therapeutic systems capable of adapting interventions dynamically to both physiological and psychological states. These trends are examined in the context of emerging clinical and consumer use cases, with a focus on scalability, personalization, and data security. By grounding these insights in current research trajectories, this work positions wearable computing as a cornerstone of preventive, personalized, and participatory healthcare. Addressing identified technical and ethical challenges will be essential for the next generation of systems to become trusted, equitable, and clinically indispensable tools. Full article

Currently, environmental issues negatively affect both firm performance and economic development, prompting society to expect enterprises to address these issues more effectively. In response, organizations, particularly manufacturing enterprises, have begun to adopt green innovation. This study examines how humble leadership in enterprise management affects organizational green innovation performance. Additionally, this study explores the mediating role of the organizational caring ethical climate and the moderating roles of the organizational structure and unabsorbed organizational resource slack. This study involved top managers from 357 manufacturing enterprises in Zhejiang Province, yielding 306 valid questionnaires. The Hierarchical regression technique is used to analyze the survey data. An analysis of the data shows that humble leadership positively affects organizational green innovation performance, with the organizational caring ethical climate serving as a mediator. Furthermore, the organizational structure and organizational resource slack positively moderate the effect of the organizational caring ethical climate on green innovation performance. This study validates and enriches social learning theory; social exchange theory; conservation of resource theory; and ability, motivation, and opportunity theory. It also provides new insights into the relationship between humble leadership and green innovation performance and expands research on the moderators of the relationship between the organizational caring ethical climate and green innovation performance. The findings suggest that managers of manufacturing enterprises should adopt humble leadership, promote a caring ethical climate, and enhance cooperation with stakeholders. Full article

The rapid expansion of Artificial Intelligence in Education (AIED) has created both remarkable opportunities and pressing concerns. Applications of intelligent tutoring systems, learning analytics, generative models, and educational robotics illustrate the transformative momentum of the field, yet they also raise fundamental questions regarding ethics, equity, and sustainability. The mission of AI in Education (MDPI) is to provide a rigorous, interdisciplinary, and inclusive platform where these debates can unfold. The journal bridges pedagogy and engineering, welcomes both empirical evidence of positive impacts and critical examinations of systemic risks, and advances responsible innovation in real educational settings. By integrating methodological standards, governance perspectives, and pedagogical ethics, including teacher-centered validation approaches, AI in Education positions itself as a space for constructive dialogue that values both enthusiasm and critique. Above all, the journal is committed to a human-centered vision for AIED, so that innovation in classrooms remains grounded in care, responsibility, and educational purpose. Full article

Historically, colorectal liver metastases (CRLMs) have been considered a contraindication for liver transplantation (LT), primarily due to limited organ availability and concerns about oncologic efficacy. However, emerging evidence indicates that highly selected patients with unresectable CRLM can achieve long-term survival following LT—often with outcomes superior to those obtained through conventional systemic therapies. To evaluate the evolving role of LT in this setting, we conducted a scoping review of the literature. A comprehensive search was performed across PubMed, Embase, Web of Science, Scopus, and ClinicalTrials.gov, as well as ProQuest Dissertations & Theses and Google Scholar to capture gray literature. The search included English-language articles published between January 2015 and April 2025. Eligible studies included those reporting on the application of LT for patients with unresectable CRLM. This scoping review synthesizes current evidence on patient selection criteria, overall and disease-free survival, recurrence patterns, and emerging biomarkers that may guide transplant eligibility. In addition, we explore innovations in organ utilization—including living donor LT and machine perfusion technologies—that aim to expand access while addressing ethical concerns related to organ allocation. As LT for CRLM transitions from investigational use to clinical implementation, this review outlines the key challenges and future opportunities that will shape its role in the landscape of transplant oncology. Full article

Recent advancements in the healthcare sector have reached a pivotal juncture, catalysed by the emergence of Digital Twin (DT) technologies. These innovations facilitate the development of virtual replicas that accurately simulate real-world conditions, thereby transforming traditional approaches to medical analysis, diagnostics, and treatment planning. Although widely successful in manufacturing, the adoption of Digital Twins in healthcare is relatively limited, particularly regarding their impact on clinical efficiency and patient outcomes. This study addresses three primary research questions: (1) How does Digital Twin technology improve individualised patient treatments and care quality? (2) What is the role of Digital Twin technology in accurately predicting patient responses to medical interventions? (3) What are the significant challenges of integrating Digital Twin technology into healthcare? Synthesising findings from 70 peer-reviewed articles, this review identifies critical knowledge gaps and provides practical recommendations for healthcare stakeholders to effectively navigate these challenges. This research proposes a conceptual framework illustrating the lifecycle of Digital Twin implementation in healthcare and outlines essential strategies for successful adoption. It emphasises the importance of robust infrastructure, clear regulatory guidance, and ethical practices to fully leverage the advantages of DT technologies. Nevertheless, this review acknowledges its limitations, including reliance on secondary data and the absence of empirical validation. Future research should focus on practical applications, diverse healthcare contexts, and broader stakeholder perspectives to comprehensively assess real-world impacts. Full article

Artificial intelligence (AI) is revolutionarily shaping the entire landscape of medicine and particularly the privileged field of radiology, since it produces a significant amount of data, namely, images. Currently, AI implementation in radiology is continuously increasing, from automating image analysis to enhancing workflow management, and specifically, pediatric neuroradiology is emerging as an expanding frontier. Pediatric neuroradiology presents unique opportunities and challenges since neonates’ and small children’s brains are continuously developing, with age-specific changes in terms of anatomy, physiology, and disease presentation. By enhancing diagnostic accuracy, reducing reporting times, and enabling earlier intervention, AI has the potential to significantly impact clinical practice and patients’ quality of life and outcomes. For instance, AI reduces MRI and CT scanner time by employing advanced deep learning (DL) algorithms to accelerate image acquisition through compressed sensing and undersampling, and to enhance image reconstruction by denoising and super-resolving low-quality datasets, thereby producing diagnostic-quality images with significantly fewer data points and in a shorter timeframe. Furthermore, as healthcare systems become increasingly burdened by rising demands and limited radiology workforce capacity, AI offers a practical solution to support clinical decision-making, particularly in institutions where pediatric neuroradiology is limited. For example, the MELD (Multicenter Epilepsy Lesion Detection) algorithm is specifically designed to help radiologists find focal cortical dysplasias (FCDs), which are a common cause of drug-resistant epilepsy. It works by analyzing a patient’s MRI scan and comparing a wide range of features—such as cortical thickness and folding patterns—to a large database of scans from both healthy individuals and epilepsy patients. By identifying subtle deviations from normal brain anatomy, the MELD graph algorithm can highlight potential lesions that are often missed by the human eye, which is a critical step in identifying patients who could benefit from life-changing epilepsy surgery. On the other hand, the integration of AI into pediatric neuroradiology faces technical and ethical challenges, such as data scarcity and ethical and legal restrictions on pediatric data sharing, that complicate the development of robust and generalizable AI models. Moreover, many radiologists remain sceptical of AI’s interpretability and reliability, and there are also important medico-legal questions around responsibility and liability when AI systems are involved in clinical decision-making. Future promising perspectives to overcome these concerns are represented by federated learning and collaborative research and AI development, which require technological innovation and multidisciplinary collaboration between neuroradiologists, data scientists, ethicists, and pediatricians. The paper aims to address: (1) current applications of AI in pediatric neuroradiology; (2) current challenges and ethical considerations related to AI implementation in pediatric neuroradiology; and (3) future opportunities in the clinical and educational pediatric neuroradiology field. AI in pediatric neuroradiology is not meant to replace neuroradiologists, but to amplify human intellect and extend our capacity to diagnose, prognosticate, and treat with unprecedented precision and speed. Full article

Background: The emergency medical services (EMS) sector, as a complex system, presents substantial hurdles in providing excellent treatment while operating within limited resources, prompting greater adoption of artificial intelligence (AI) as a tool for improving operational efficiency. While AI models have proved beneficial in healthcare operations, there is limited explainability and interpretability, as well as a lack of data used in their application and technological advancement. Methods: The scoping review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines for scoping reviews, using PubMed, IEEE Xplore, and Web of Science, with a procedure of double screening and extraction. The search included articles published from 2018 to the beginning of 2025. Studies were excluded if they did not explicitly identify an artificial intelligence (AI) component, lacked relevance to emergency department (ED) or prehospital contexts, failed to report measurable outcomes or evaluations, or did not exploit real-world data. We analyzed the data source used, clinical subclasses, AI domains, ML algorithms, their performance, as well as potential roles for large language models (LLMs) in future applications. Results: A comprehensive PRISMA-guided methodology was used to search academic databases, finding 1181 papers on prehospital emergency treatment from 2018 to 2025, with 65 articles identified after an extensive screening procedure. The results reveal a significant increase in AI publications. A notable technological advancement in the application of AI in EMS using different types of data was explored. Conclusions: These findings highlighted that AI and ML have emerged as revolutionary innovations with huge potential in the fields of healthcare and medicine. There are several promising AI interventions that can improve prehospital emergency care, particularly for out-of-hospital cardiac arrest and triage prioritization scenarios. Implications for EMS Practice: Integrating AI methods into prehospital care can optimize the use of available resources, as well as triage and dispatch efficiency. LLMs may have the potential to improve understanding and assist in decision-making under pressure in emergency situations by combining various forms of recorded data. However, there is a need to emphasize continued research and strong collaboration between AI experts and EMS physicians to ensure the safe, ethical, and effective integration of AI into EMS practice. Full article

Artificial intelligence (AI) is emerging as a valuable complementary tool in small-molecule drug discovery, augmenting traditional methodologies rather than replacing them. This review examines the evolution of AI from early rule-based systems to advanced deep learning, generative models, diffusion models, and autonomous agentic AI systems, highlighting their applications in target identification, hit discovery, lead optimization, and safety prediction. We present both successes and failures to provide a balanced perspective. Notable achievements include baricitinib (BenevolentAI/Eli Lilly, an existing drug repurposed through AI-assisted analysis for COVID-19 and rheumatoid arthritis), halicin (MIT, preclinical antibiotic), DSP-1181 (Exscientia, discontinued after Phase I), and ISM001-055/rentosertib (Insilico Medicine, positive Phase IIa results). However, several AI-assisted compounds have also faced challenges in clinical development. DSP-1181 was discontinued after Phase I, despite a favorable safety profile, highlighting that the acceleration of discovery timelines by AI does not guarantee clinical success. Despite progress, challenges such as data quality, model interpretability, regulatory hurdles, and ethical concerns persist. We provide practical insights for integrating AI into drug discovery workflows, emphasizing hybrid human-AI approaches and the emergence of agentic AI systems that can autonomously navigate discovery pipelines. A critical evaluation of current limitations and future opportunities reveals that while AI offers significant potential as a complementary technology, realistic expectations and careful implementation are crucial for delivering innovative therapeutics. Full article

Sierra Leone possesses distinct educational, economic, and social characteristics. Public universities in the country, funded by the government, are mandated to promote sustainable development, ethical conduct, and social welfare, aligning with national development strategies such as the Midterm National Development Plan and the Education Sector Plan, which emphasize leadership, diversity, and ethical standards to advance sustainable development practices. This study applies Transformational Leadership Theory to investigate the influence of transformational leadership on corporate social responsibility, exploring the mediating role of innovative work behavior and the moderating effect of organizational culture on this relationship. Using a stratified sampling technique, data were collected from 367 employees across six public universities in Sierra Leone and analyzed with SMART PLS software 4.1.1.2. The findings revealed that transformational leadership positively and significantly impacts corporate social responsibility and innovative work behavior, with innovative work behavior partially mediating the relationship between transformational leadership and corporate social responsibility, while organizational culture positively and significantly moderates this relationship. Based on these findings, the study recommends that public universities in Sierra Leone integrate transformational leadership principles into their institutional frameworks to improve organizational outcomes and leadership effectiveness. This can be achieved through leadership development programs emphasizing transformational attributes such as inspirational motivation, individualized consideration, and vision-sharing, alongside mentorship programs for leaders at all levels to strengthen leadership skills and foster an organizational culture aligned with institutional goals. Full article

This study introduces the Human-AI Symbiotic Theory (HAIST), designed to guide authentic collaboration between human researchers and artificial intelligence in academic contexts, while pioneering a novel AI-assisted approach to theory validation that transforms educational research methodology. Addressing critical gaps in educational theory and advancing validation practices, this research employed a sequential three-phase mixed-methods approach: (1) systematic theoretical synthesis integrating five paradigmatic perspectives across learning theory, cognition, information processing, ethics, and AI domains; (2) development of an innovative validation framework combining three established theory-building approaches with groundbreaking AI-assisted content assessment protocols; and (3) comprehensive theory validation through both traditional multi-framework evaluation and novel AI-based content analysis demonstrating unprecedented convergent validity. This research contributes both a theoretically grounded framework for human-AI research collaboration and a transformative methodological innovation demonstrating how AI tools can systematically augment traditional expert-driven theory validation. HAIST provides the first comprehensive theoretical foundation designed explicitly for human-AI partnerships in scholarly research with applicability across disciplines, while the AI-assisted validation methodology offers a scalable, reliable model for theory development. Future research directions include empirical testing of HAIST principles in live research settings and broader application of the AI-assisted validation methodology to accelerate theory development across educational research and related disciplines. Full article

Since the advent of generative AI, research on AI in Education (AIEd) has experienced explosive growth. This study systematically explores publication dynamics, keyword evolution, and research focuses in AIEd by analyzing 2952 papers from the Web of Science (1990–2024). Using bibliometric methods, 2800 English publications were screened, with analyses conducted via VOSviewer v1.6.20 and Python v3.11.5. Findings show a surge in publications post-2020, reaching 612 in 2023 and 1216 by November 2024. The US and China are leading contributors, with the University of London and the University of California system as core institutions. Keywords evolved from “AI” and “machine learning” (2018–2020) to “ChatGPT” and “ethics” (post-2022), reflecting dual focuses on technological applications and ethical considerations. Notably, 68% of highly cited papers address ethical controversies, while higher education and medical education emerge as primary application domains, involving personalized learning and intelligent tutoring systems. Cross-disciplinary research is evident, with education studies comprising the largest category. The study reveals AIEd’s shift toward socio-technical integration, highlighting generative AI’s transformative role yet identifying gaps in ethical governance and K-12 research. These insights inform policymakers, journals, and institutions, advocating for enhanced interdisciplinary collaboration and long-term impact research to balance innovation with educational ethics. Full article