AI ethics in January 2026

Biomedical research models are undergoing continuous evolution, while conventional models (two-dimensional/ three-dimensional cultures and animal studies) face limitations in physiological relevance and ethical constraints. Against this backdrop, the integration of organ-on-a-chip (OoC) technology with multi-omics methodologies is driving a profound paradigm shift in the field. OoC platforms utilize microfluidic technology to construct biomimetic three-dimensional microenvironments capable of hi

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 evi

Introduction The blue economy has emerged as a strategic framework for aligning marine-based economic development with environmental sustainability and social equity. Empirical evidence from arid and industrialized coastal regions, however, remains limited. Methods This study employs a convergent mixed-methods design using a structured questionnaire administered to 404 stakeholders across the Eastern Province coastline of Saudi Arabia, complemented by qualitative open-ended responses. Quantitati

As artificial intelligence (AI) becomes increasingly embedded in social life, understanding its interpersonal and psychological implications is urgent yet undertheorized. This article introduces the machine-integrated relational adaptation (MIRA) model, a transdisciplinary, middle-range theoretical framework that provides a foundational account of when, how, and why AI functions as a relational entity in human ecosystems. MIRA distinguishes two crucial roles of AI: relational partner (direct-int

Introduction: Vaginal candidiasis remains a recurrent fungal infection affecting millions of women worldwide, necessitating innovative local delivery systems to overcome poor drug solubility and mucosal barriers. This study introduces Dual-Functional Hybrid Cationic PEGylated Proniosomes (DHCPP) as a smart nano-platform designed to boost the vaginal delivery of Fenticonazole Nitrate (FTN). Methods: DHCPP systems were fabricated via the coacervation phase separation method and optimized using a f

BACKGROUND: Early detection of cardiovascular disease is a global public health priority. Artificial intelligence (AI)-enabled stethoscopes offer robust performance characteristics in point-of-care detection of heart failure, atrial fibrillation, and valvular heart disease (VHD). We conducted a pragmatic, cluster-randomised controlled implementation trial to determine the real-world effect and implementation challenges of AI-stethoscopes. METHODS: UK primary care practices were cluster randomise

CRISPR-Cas genome-editing technologies have emerged as powerful tools for precise DNA and RNA modulation, offering promising therapeutic strategies for neurodegenerative disorders such as Alzheimer's disease (AD), Parkinson's disease (PD), Huntington's disease (HD), and amyotrophic lateral sclerosis (ALS). This review critically evaluates current CRISPR/Cas applications in neurodegeneration, with emphasis on mechanistic insights, therapeutic outcomes, and translational feasibility. Preclinical a

Abstract Teacher education students play double role as present learners and future educators. Hence, they need targeted training to navigate the growing influence of Generative Artificial Intelligence (GenAI) on teaching, learning and professional identity. However, existing artificial intelligence (AI) literacy programmes predominantly emphasize technical AI knowledge and pre‐GenAI tools or are offered by GenAI platforms that focus on their own technologies' features, thereby lacking pedagogic

Objectives: This study aims to evaluate whether contemporary artificial intelligence (AI), including convolutional neural networks (CNNs) for medical imaging and large language models (LLMs) for language processing, could replace physicians in the near future and to identify the principal clinical, technical, and regulatory barriers. Methods: A narrative review is conducted on the scientific literature addressing AI performance and reproducibility in medical imaging, LLM competence in medical kn

Artificial intelligence (AI) is entering routine radiology practice, but most studies evaluate algorithms in isolation rather than their interaction with radiologists in clinical workflows. This narrative review summarizes current knowledge on human-AI interaction in radiology and highlights practical risks and opportunities for clinical teams. First, simple conceptual models of human-AI collaboration are described, such as diagnostic complementarity, which explain when radiologists and AI can a

Abstract The rapid development of digital technologies, including AI, is having a significant impact on the social, economic, and political life. Yet, while presented as milestones in innovation and progress, these technological transformations have also introduced mechanisms of control, forms of organization, and ideological patterns that bear striking resemblances to historical fascist phenomena. Moreover, in parallel and increasingly intersecting with this development is a broader political c

Background: Contemporary stroke rehabilitation protocols traditionally emphasise session frequency and treatment duration over intervention intensity-yet emerging evidence suggests we may be preparing patients for therapeutic marathons when their brains demand neuroplastic sprints. Across neuroscientific, behavioural, and clinical domains, convergent data indicate that repetition density, metabolic load, engagement, and temporal compression-not cumulative minutes-constitute the biologically mean

Introduction: Treatment-Resistant Depression (TRD) remains a major challenge in the management of Major Depressive Disorder (MDD). Esketamine, the S-enantiomer of ketamine and a glutamatergic modulator, was approved by the FDA and EMA for TRD in 2019. Beyond its rapid antidepressant effects, esketamine may enhance neuroplasticity, facilitating the reconnection with emotional and cognitive processes, improving mentalization and social cognition, and promoting resilience. Objective: This prospecti

Objective: Evidence regarding the association between the total cholesterol, high-density lipoprotein, and glucose (CHG) index and diabetic retinopathy (DR) remains limited. This study aimed to explore the relationship between CHG and the prevalence of DR and evaluate its discriminative ability for DR. Methods: This cross-sectional study analyzed data from 1,909 individuals with diabetes mellitus (DM), aged 45-90 years, whose information was collected between August and December 2011. To determi

Introduction: Rapid adoption of Artificial Intelligence (AI) in learning has revolutionized learners' engagement but comprehension of psychological and technological drivers of successful AI-enabled learning remains scarce. This research investigates how students' perceived agency of AI, usefulness, ease of use, trust, autonomy supporting, and self-efficacy collectively impact students' self-learning behavior and motivation. Based on Technology Acceptance Model (TAM), Social Cognitive Theory (SC

The recent surge of Large Language Models (LLMs) has led to claims that they are approaching a level of creativity akin to human capabilities. This idea has sparked a blend of excitement and apprehension. However, a critical piece that has been missing in this discourse is a systematic evaluation of LLMs' semantic diversity, particularly in comparison to human divergent thinking. To bridge this gap, we leverage recent advances in computational creativity to analyze semantic divergence in both st

Abstract This study explores variables associated with teachers’ Artificial Intelligence (AI) literacy, a key competency for effective and responsible AI integration in education. A total of 270 teachers completed an online survey including measures of AI literacy, AI acceptance, computational thinking, AI anxiety, and digital divide. Results revealed that all AI acceptance variables were positively associated with AI literacy, with hedonic motivation and willingness to use AI emerging as the st

Digital media, including television, the internet, social media, video games, and interactive assistants, form the digital ecosystem. When this digital ecosystem is designed with children's unique developmental needs in mind, it can support learning and well-being. In contrast, digital ecosystems that prioritize engagement and commercialization often encourage prolonged use, which in turn can displace healthy behaviors (eg, movement behaviors, sleep), and contribute to negative outcomes. This po

Purpose: The purpose of this research case study is to argue that the structured path of devotion and virtues outlined in the Twelfth Chapter of the Bhagavad Gita provide a universal framework for cultivating ethical individuals. It aims to demonstrate how this framework serves as a powerful tool for addressing modern societal challenges such as polarization and ethical apathy. Ultimately, the study seeks to guide the development of a conscious, compassionate, and harmonious global society throu

ABSTRACT As an emerging branch of clinical medicine, microbiota medicine has attracted worldwide attention from clinicians, medical educators, patient communities, and industry. However, this developing field still lacks consensus on its fundamental principles as well as guidelines for clinical and educational practice. An expert panel was convened by the journal Microbiota Medicine Research at the 2025 CHINAGUT Conference to develop the principles and practice guidelines of microbiota medicine

Introduction: The European Health Data Space (EHDS), the European Union's new regulatory framework for health data use and reuse, will have important implications for citizens across the Union. While the regulation aims to empower citizens in the primary use of their health data-such as by giving them access to their electronic health records-their role in the secondary use of health data remains less clearly defined. Methods: To explore this, we interviewed health data experts across 23 Europea

Sepsis is a major global health crisis where early recognition and effective management remain significant challenges for healthcare systems. As part of the Lausanne University Hospital sepsis quality of care program, we developed and validated an Artificial Intelligence (AI)-powered Sepsis Learning Health System (SLHS) to enhance sepsis care. The SLHS combines a standardized clinical pathway with HERACLES, an AI algorithm that retrospectively classifies patient data into confirmed, possible, or

Introduction: With the proliferation of generative artificial intelligence (AI) in higher education, student overreliance has become a growing concern, potentially undermining critical thinking and autonomous learning. To address the lack of a comprehensive measurement tool, this study developed and validated the AI Dependence Scale (AIDep-22), a new instrument designed to assess this phenomenon across four hypothesized dimensions: emotional dependence, functional dependence, cognitive dependenc

While the extant research has provided a recipe for researchers to undertake thematic analysis (TA) in a theoretically and methodologically sound way, there has not yet been sufficient research to map out TA in the age of generative artificial intelligence (Gen AI). Building on and refining my 2020 article Applying thematic analysis to education: A hybrid approach to interpreting data in practitioner research published in International Journal of Qualitative Methods , which provides an example o

In recent years, much of the research on clustering algorithms has primarily focused on enhancing their accuracy and efficiency, frequently at the expense of interpretability. However, as these methods are increasingly being applied in high-stakes domains such as healthcare, finance, and autonomous systems, the need of transparent and interpretable clustering outcomes has become a critical concern. This is not only necessary for gaining user trust but also for satisfying the growing ethical and

The integration of generative AI tools, such as ChatGPT, Gemini, and DeepSeek, into higher education offers transformative opportunities for personalised learning and academic productivity. However, their unregulated use raises concerns about academic integrity, critical thinking, and educational equity. This systematic review synthesises insights from 96 peer-reviewed articles, identifying six key intervention themes, namely, curriculum integration, policy and governance, faculty development, s

Background: Metabolic syndrome, a clinical condition defined by central obesity, impaired glucose regulation, elevated blood pressure, hypertriglyceridemia, and low high-density lipoprotein cholesterol across the lifespan, is now a major public health issue typically managed with lifestyle, behavioral, and dietary recommendations. However, “one-size-fits-all” recommendations often yield modest, heterogeneous responses and poor long-term adherence, creating a clinical need for more targeted and i

This study aims to deconstruct the complex relationship between artificial intelligence anxiety and generative AI adoption intention in the context of higher education. An extended analytical framework is constructed by integrating the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT). Taking faculty and students from three universities in Sichuan Province as the research objects, data are collected through a questionnaire survey, and empirical

Introduction: Physical literacy is crucial for promoting lifelong engagement in physical activity. In response to rising childhood inactivity, this study explores the impact of traditional children's games on physical literacy within a school context. Methods: A quasi-experimental design was applied involving 60 students (aged 11-12) from two schools in Trabzon, Turkey. The experimental group participated in an 8-week program of culturally-rooted traditional games. The control group followed sta

Generative Artificial Intelligence (GenAI) models produce increasingly sophisticated outputs, yet their underlying mechanisms remain opaque. To clarify how explainability is conceptualized and implemented in GenAI research, this two-stage review systematically examined 261 articles retrieved from six major databases. After removing duplicates and applying predefined inclusion criteria, 63 articles were retained for full analysis. In the first stage, an umbrella review synthesized insights from 1

The rise of artificial intelligence (AI) in education, particularly generative AI, challenges the sufficiency of the established Technological Pedagogical Content Knowledge (TPACK) framework. AI’s agentic autonomy, epistemic complexities, and ethical dimensions necessitate an evolved model. This study investigates the newly proposed Intelligent-TPACK (I-TPACK) framework, designed to address these gaps by integrating five knowledge domains: AI-Technological, AI-Content, AI-Pedagogical, Human-AI C

Multiple articles have touted the longitudinal promise of artificial intelligence (AI) in radiology, including projections of streamlining repetitive tasks, improving workflow, and reducing physician burnout. The purpose of this article is to review publications directly assessing the impact of AI on radiologist burnout and the impact of AI on the established drivers of radiologist burnout. Our analysis found conflicting, inconclusive limited data that AI reduces radiologist burnout, and the bal

Large language models (LLMs) are emerging as powerful tools in healthcare, with a growing role in global health, particularly in low- and middle-income countries (LMICs). This Perspective examines the current progress, challenges and prospects of LLMs in addressing health system disparities and supporting the achievement of the Sustainable Development Goals (SDGs). While high-income countries dominate the development and deployment of LLMs, LMICs face substantial barriers. These include limited

This article examines the impact of data analytics, artificial intelligence (AI) technology, and cloud computing on HR efficiency of Jordanian organizations, with emphasis on the moderating effects of information quality. A quantitative approach was utilized, and structured surveys were distributed to HR experts and HR managers who work in different industrial sectors in Jordan. Data from 415 valid respondents were statistically analyzed rigorously using the statistical software SPSS and AMOS, a

Abstract The widespread adoption of large language models (LLMs) raises important questions about their safety and alignment 1 . Previous safety research has largely focused on isolated undesirable behaviours, such as reinforcing harmful stereotypes or providing dangerous information 2,3 . Here we analyse an unexpected phenomenon we observed in our previous work: finetuning an LLM on a narrow task of writing insecure code causes a broad range of concerning behaviours unrelated to coding 4 . For

Despite decades of study, large parts of the mammalian metabolome remain unexplored1. Mass spectrometry-based metabolomics routinely detects thousands of small molecule-associated peaks in human tissues and biofluids, but typically only a small fraction of these can be identified, and structure elucidation of novel metabolites remains challenging2–4. Biochemical language models have transformed the interpretation of DNA, RNA and protein sequences, but have not yet had a comparable impact on unde

Introduction: Artificial intelligence (AI) holds profound potential to reshape public health through enhanced disease prediction, diagnosis, and health management. However, this technological advancement is accompanied by significant ethical, social, and governance challenges. This systematic review aims to comprehensively examine the opportunities and challenges of AI in public health, focusing on its applications, associated dilemmas, and governance pathways. Methods: This review was conducted

Purpose: The purpose of this research case study is to examine the Vibhuti Yoga of the Tenth Chapter of the Bhagavad Gita as a philosophical framework for cultivating a sacramental vision that perceives the divine as immanent within the natural and social world. It seeks to interpret how Krishna’s enumeration of divine manifestations provides a non-utilitarian ethical foundation for ecological responsibility, mental well-being, and value-based human conduct. Further, the study aims to contextual

D-amino acid substitution provides an effective strategy for optimizing antimicrobial peptides (AMPs) by enhancing their stability. However, the absence of universal rules renders traditional screening methods time-consuming and labor-intensive, potentially leading to reduced or complete loss of activity. Here, we curated a D-amino acid-substituted AMP dataset from published literature and databases. We then developed ADAPT, an AI-based tool for predicting the functional impact of D-amino acid s

By leveraging everyday technologies such as mobile apps, wearables, and AI-enabled tools, digital health interventions (DHIs) offer new pathways to integrate self-management and intervention programs into the fabric of daily life, while bridging gaps in care through continuous, context-aware support. Yet many tools underperform clinically because digital engagement ("screen time") is conflated with impact, while behavioral science is retrofitted, if applied at all. We propose the ENGAGE Framewor

Introduction Despite the potential of artificial intelligence (AI) to transform healthcare delivery and reduce costs, adoption remains uneven across populations. Understanding the demographic, behavioral, and cognitive factors influencing public willingness to use AI-powered health tools is critical for equitable implementation. This study examined determinants of AI adoption in healthcare among adults in the United States (U.S.). Methods A cross-sectional survey was conducted between March and

BACKGROUND: Genioplasty and chin-augmentation are well-established procedures aimed at enhancing lower facial aesthetics. Traditionally, aesthetic outcomes have been assessed subjectively through expert opinions and patient-reported measures. The integration of artificial intelligence (AI) offers an objective approach to evaluating surgical results. This study utilizes the ICA Aesthetic Navigation AI Research Metrics Model (ICAAN® ARMM) to analyze postoperative changes in facial attractiveness,

Introduction: Major depressive disorder (MDD) is a complex neuropsychiatric disorder with multifactorial origins involving oxidative stress, neuroinflammation, neurotransmitter imbalance, and HPA axis dysfunction. Conventional treatments are often limited by side effects and suboptimal efficacy, confirming the need for alternative therapies. This study investigates the antidepressant-like and neuroprotective potential of selenium nanoparticles biosynthesized using epigallocatechin gallate (SeNPs

While nutraceuticals hold great promise for improving health, their efficacy is often limited by the poor stability and low bioavailability of many bioactive compounds. Nanoencapsulation has emerged as a transformative solution to these challenges, involving nanoscale carriers that protect sensitive nutrients from degradation and enhance their absorption. This review provides a comprehensive overview of nanoencapsulation strategies in the food and nutrition domain. We outline the historical deve

Artificial intelligence (AI) is reshaping how learning environments are designed and experienced, offering new possibilities for personalization, creativity, and immersive engagement. This systematic review synthesizes 43 empirical studies (Scopus, Web of Science) to examine the training needs and practices of primary and secondary education teachers for effective AI integration and overall professional development (PD). Following PRISMA guidelines, the review gathers teachers’ needs and practic

Underwater tasks such as ocean exploration and emergency rescue demand advanced wearable sensors. However, multifunctional underwater sensors capable of integrating self-powered signal transmission, effective thermal-moisture regulation, and multi-signal decoupling remain unreported. Here, we present a three-dimensional multi-functional thermoelectric device composed of highly porous polyurethane foam coated with a waterproof conductive layer made from single-walled carbon nanotubes, poly(3,4-et

Effective blood glucose management is an increasing demand worldwide. Traditional solutions separate glucose detection and insulin delivery, which is less efficient compared to emerging closed-loop wearable systems controlled by continuous glucose monitors (CGMs). However, CGM-controlled systems raise new safety risks, as false CGMs readings can cause insulin overdose, which results in hypoglycemia and fatal consequences. This work proposes a concept of a dual closed-loop insulin delivery system

With the rapid advancement of educational digitalization, pre-service teachers' digital competence has become a critical prerequisite for adapting to modern teaching practices and promoting high-quality education. This systematic review provides a comprehensive analysis of research on the digital competence of pre-service teachers over the past decade (2014-2024). Drawing on 38 studies retrieved from the Web of Science and Scopus databases, it examines several key aspects, including publication

The rapid integration of Artificial Intelligence (AI) in India’s banking sector offers operational benefits but also raises sustainability challenges. This study focuses on “Green AI,” defined as AI technologies optimized for energy efficiency and carbon conscious practices, by extending the Technology–Organization–Environment (TOE) and Technology Acceptance Model (TAM) frameworks with sustainability-linked factors. Data were collected from 412 mid- to senior-level professionals across six leadi

High-throughput proteomics is critical for understanding biological processes, enabling large-scale studies such as biomarker discovery and systems biology. However, current mass spectrometry technologies face limitations in speed, sensitivity, and scalability for analyzing large sample cohorts. The Thermo Scientific Orbitrap Astral Zoom mass spectrometer (MS) was developed to address these limitations by improving acquisition speed, ion utilization, and spectral processing, which are all essent

INTRODUCTION: This study evaluated the effectiveness of Moderna's updated mRNA-1273 vaccine targeting the KP.2 variant, compared to people who did not receive any 2024-2025 COVID-19 vaccine, in preventing COVID-19-associated hospitalizations and medically-attended COVID-19 among adults aged ≥ 18 years in the United States during the 2024-2025 season. METHODS: Data were extracted from linked administrative healthcare claims and electronic health records (EHR) for vaccinations from 23 August 2024

3D Concrete Printing (3DCP) is reshaping construction by enabling automated fabrication, reducing material waste, and supporting more sustainable building solutions. This study presents a bibliometric and thematic analysis of global 3DCP research from 2015 to April 2025 using data from the ScienceDirect and Dimensions databases. VOSviewer was employed to map research trends, country collaborations, and thematic clusters. The review identifies four main focus areas: (I) advancements in 3DCP techn

To compare the diagnostic performance of a subscription-based medical large language model (LLM) certified as a medical device (Prof. Valmed), a subscription-based general-purpose LLM (ChatGPT-5 Thinking), and a freely accessible medical LLM (OpenEvidence). Sixty vignettes covering rare rheumatic diseases and differential diagnoses were entered using a standardized prompt to generate five top diagnoses and respective diagnostic probabilities. Blinded rheumatologists categorized suggested diagnos

Introduction: Artificial intelligence (AI) technology has significantly changed human work. Increasingly, organizations are promoting the integration of AI into employees' work processes. While existing research has explored AI applications in the workplace, relatively little attention has been devoted to understanding how organizational AI adoption influences employees' motivational reactions and the subsequent impacts. Drawing on approach-avoidance motivational theory, this research explores t

This manifesto presents the theoretical and operational foundation of the Ze System—a radical framework for the scientific investigation of latent reality. Moving beyond the paradigm of passive observation, it posits that a substantial portion of reality exists in an unmanifested, wave-like state of potentialities, statistical shadows, and distributed correlations. Ze redefines scientific inquiry as an active, provocative engagement with this latent field. Its core thesis is that the hidden is n

As AI-based technologies such as ChatGPT are increasingly used across various sectors, understanding how people conceptualize artificial intelligence (AI) is crucial for anticipating public response and developing AI technologies responsibly 1. We hypothesize that public perceptions of AI are rapidly evolving, and that these perceptions inform not only how people use AI, but also the extent to which they trust it and the role they believe it should play in their lives - if at all. However, belie

Background: Diabetic kidney disease (DKD) is a major microvascular complication of diabetes, often progressing silently and leading to end-stage kidney disease (ESKD) and cardiovascular morbidity. Early identification and risk-adapted intervention are crucial to improving long-term outcomes, yet existing clinical workflows are limited by delayed diagnosis and underutilization of available therapies. Methods: We propose and evaluate a multimodal, risk-driven framework for the early recognition an

Abstract Exponential growth in scientific literature has heightened the demand for efficient evidence-based synthesis, driving the rise of the field of automated meta-analysis (AMA) powered by natural language processing and machine learning. This PRISMA systematic review introduces a structured framework for assessing the current state of AMA, based on screening 13,216 papers (2006–2024) and analyzing 61 studies across diverse domains. Findings reveal a predominant focus on automating data proc

The severe shortage of donor organs and limitations of current disease models highlight the urgent need for transformative strategies in tissue engineering (TE) and regenerative medicine (RM). Bioprinting has emerged as a powerful approach for creating functional tissues and organs, yet current workflows remain labor-intensive, variable, and challenging to scale. The convergence of artificial intelligence (AI), advanced bioprinting technologies, robotics, biosensing, and cutting-edge biological

Over the last 15 years, mixture risk assessment for food xenobiotics has evolved from conceptual discussions and simple screening tools, such as the Hazard Index (HI), towards operational, component-based and probabilistic frameworks embedded in major food-safety institutions. This review synthesizes methodological and regulatory advances in cumulative risk assessment for dietary "cocktails" of pesticides, contaminants and other xenobiotics, with a specific focus on food-relevant exposure scenar

Artificial intelligence is increasingly being used in medical practice to complete tasks that were previously completed by the physician, such as visit documentation, treatment plans and discharge summaries. As artificial intelligence becomes a routine part of medical care, physicians increasingly trust and rely on its clinical recommendations. However, there is concern that some physicians, especially those younger and less experienced, will become over-reliant on artificial intelligence. Over-

The rapid deployment of generative language models has raised concerns about social biases affecting the well-being of diverse consumers. The extant literature on generative language models has primarily examined bias via explicit identity prompting. However, prior research on bias in language-based technology platforms has shown that discrimination can occur even when identity terms are not specified explicitly. Here, we advance studies of generative language model bias by considering a broader

Objectives: This study aims to elucidate the pivotal role of Artificial Intelligence (AI) in driving the transformation of precision medicine, comprehensively analyzing how it reshapes healthcare systems from traditional diagnosis and treatment paradigms into personalized health management ecosystems. Methods: A comprehensive narrative review was conducted to systematically synthesize and critically evaluate the innovative applications, paradigm shifts, and future prospects of AI across the enti

Precision healthcare is increasingly oriented toward the development of therapeutic strategies that are as individualized as the patients receiving them. Central to this paradigm shift is artificial intelligence (AI)-enabled multi-modal data integration, which consolidates heterogeneous data streams-including genomic, transcriptomic, proteomic, imaging, environmental, and electronic health record (EHR) data into a unified analytical framework. This integrative approach enhances early disease det

This study examines how SMEs adopt AI using a qualitative design informed by Socio-Technical Systems Theory. The findings indicate that AI adoption is shaped by the interaction of technical constraints, organizational routines, and external pressures such as client expectations and policy uncertainty. Leadership engagement, data infrastructure, and workforce dynamics play a central role in influencing implementation progress. The study provides practical guidance for supporting more context-sens

Abstract Mental health disorders are becoming a major global health concern and pose a significant burden on global healthcare systems. Nearly one billion people suffer from mental disorders, accounting for 13% of the global disease burden and $1 trillion in annual productivity loss. Depression is the leading cause of disability and suicide is the second leading cause of death among young individuals. Economic uncertainty, social isolation, climate change, shifting societal norms, political conf

The development of sustainable smart cities is closely linked to the implementation of artificial intelligence in urban services, which opens up new possibilities for efficient resource management, improving the quality of life and strengthening the participation of citizens. At the same time, the question arises as to how legal and strategic frameworks can support the use of artificial intelligence in a way that contributes to environmental, social and economic sustainability in line with the o

Introduction The aim of this systematic review is to examine the scientific literature published on digital reading and writing in higher education within the field of social sciences, assisted by generative artificial intelligence. Methods The PRISMA methodology and the SALSA Framework were applied, based on a bibliographic search conducted in the Scopus and Web of Science databases. Journal articles that explicitly addressed the established topic, published between 1 January 2023 and 7 March 2

Although communication quality is widely recognized as a catalyst for workplace innovation, existing research seldom integrates communication quality, trust in leadership, and organizational commitment within a single explanatory framework, particularly in team-based enterprises operating in emerging economies. This study examines how communication quality fosters employee innovation through the sequential mediating roles of trust in leadership and organizational commitment, emphasizing its cont

Abstract Conventional electromagnetic cloaking paradigms predominantly necessitate the encasing of static objects within predefined topological enclosures, fundamentally restricting invisibility to fixed, closed geometries. Realizing dynamic, adaptive concealment for arbitrary moving targets within an open, boundary-free aperture remains a formidable challenge. Here, we report a meta-reinforcement-learning metasurface (Meta 2 Surface) that enables the first experimental demonstration of a "trans

The convergence of Artificial Intelligence (AI) and the Internet of Things (IoT) is enabling a new class of intelligent applications. Specifically, Large Language Models (LLMs) are emerging as powerful tools not only for natural language understanding but also for enhancing IoT security. However, the integration of these computationally intensive models into resource-constrained IoT environments presents significant challenges. This paper provides an in-depth examination of how LLMs can be adapt

Background Grit, defined as perseverance and passion for long-term goals, is a vital psychological trait that contributes to academic success and overall wellbeing. At the same time, regular PA supports physical and mental health, yet its engagement often declines among university students. Although grit has been linked to health-promoting behaviors, the mechanisms underlying this association remain unclear. This study investigated the mediating role of self-efficacy from a variable-centered per

The opaque nature of black-box deep learning (DL) models poses significant challenges for intrusion detection systems (IDSs) in Internet of Things (IoT) networks, where transparency, trust, and operational reliability are critical. Although explainable artificial intelligence (XAI) has been increasingly adopted to enhance interpretability, its impact on detection performance and computational efficiency in resource-constrained IoT environments remains insufficiently understood. This systematic r

DNA methylation is a central epigenetic mechanism that mediates the interaction between nutritional exposures and gene regulation. Emerging evidence demonstrates that diet, bioactive compounds, genetic background, and lifestyle factors collectively shape the human methylome, influencing metabolic function, disease susceptibility, and biological aging. This review synthesizes current knowledge on the molecular and biochemical mechanisms of DNA methylation, the role of nutrients and dietary patter

Raman spectroscopy is a non-destructive analytical technique based on molecular vibrational properties. However, its practical application is often challenged by weak scattering signals, complex spectra, and the high-dimensional nature of the data, which complicates accurate interpretation. Traditional chemometric methods are limited in handling complex, nonlinear Raman data and rely on tedious, expert-knowledge-based feature engineering. The fusion of data-driven Machine Learning (ML) and Deep

Clinical reasoning is foundational to medical practice, requiring clinicians to synthesise complex information, recognise patterns, and apply causal reasoning to reach accurate diagnoses and guide patient management. However, human cognition is inherently limited by factors such as limitations in working memory capacity, constraints in cognitive load, a general reliance on heuristics; with an inherent vulnerability to biases including anchoring, availability bias, and premature closure. Cognitiv

Objective To investigate the correlation of β-C-terminal telopeptide of type I collagen (β-CTX), procollagen type I N-terminal propeptide (PINP), alkaline phosphatase (ALP) with bone mineral density (BMD) in patients with osteoporosis and evaluate their predictive value for secondary fracture risk. Methods A total of 180 osteoporosis patients and 80 healthy controls were enrolled. The osteoporosis group was stratified into fracture and non-fracture cohorts. Correlation of β-CTX, PINP, ALP with B

This review examines the role of artificial intelligence (AI) agents in programming education, focusing on how these tools are being integrated into educational practice and their impact on student learning outcomes. An analysis of 58 peer-reviewed studies published between 2022 and 2025 identified three primary categories of AI agents: chatbots, generative AI (GenAI), and intelligent tutoring systems (ITS), with GenAI being the most frequently studied. The studies report that the primary instru

Accurate blood pressure (BP) monitoring is essential for preventing and managing cardiovascular disease. Advancements in materials science, medicine, flexible electronic, and artificial intelligence (AI) have enabled cuffless, unobtrusive BP monitoring systems, offering an alternative to traditional sphygmomanometers. However, extending these advances to real-world cardiovascular care particularly in resource-limited settings remains challenging due to constraints in computational resources, pow

Accurate Air Quality Index (AQI) classification is essential for environmental surveillance and public health decision-making. Using a publicly available daily U.S. county-level dataset with six AQI categories (Good, Moderate, Unhealthy for Sensitive Groups, Unhealthy, Very Unhealthy, Hazardous), we conducted a comprehensive benchmarking study. Data preprocessing included missing-value imputation and class balancing via Synthetic Minority Over-sampling Technique (SMOTE). We trained and evaluated

This study introduces the Islamic Ḥiwār Framework (IHF), a multi-layered, KPI-based architecture developed to operationalize intra-faith dialogue within Islamic contexts by translating theological, ethical, and institutional principles into measurable performance indicators. Drawing from Islamic epistemology, dialogical ethics, and governance structures, the IHF addresses the need for a strategic, accountable, and scalable model for religious rapprochement. A mixed-methods approach was employed,

ABSTRACT Background Despite growing attention to English education in Bangladesh, conventional classroom instruction often fails to meet learners' diverse needs or ensure effective language development. Informal digital learning of English (IDLE) has emerged as a promising avenue for fostering learners' affective experience and enhancing proficiency beyond formal classroom settings. Nevertheless, empirical evidence on how Bangladeshi learners participate in and experience self‐directed language

Financial management is the most critical aspect regarding the determination of the success or failure of the public sector within the Republic of South Africa. However, currently, there are several challenges that militate against effective as well as efficient financial management within the public sector. Traditional financial management techniques remain a major obstacle to effective and efficient service delivery in South Africa’s public sector. Legacy systems and fragmented fiscal processe

Wearable sensors are transforming real-time, non-invasive health monitoring. Despite considerable advances in electrochemical and optical sensing modalities, challenges remain in achieving reliable, sensitive, and cost-effective detection of sweat metabolites due to the variable chemical composition of sweat and difficulties in device integration. Electrochemiluminescence (ECL) offers an attractive alternative for wearable sensing due to its high sensitivity and ease of integration without compl

This study introduces the Ecumenical Christian KPI Framework (ECKF) as a strategic model for transforming theological aspirations of Christian unity into a measurable, performance-driven system. The research addresses the central challenge of operationalizing ecumenism by constructing a comprehensive framework comprising 12 strategic axes and 110 validated Key Performance Indicators (KPIs). Utilizing a mixed-methods approach, the study integrates expert Delphi analysis, document evaluation of in

Bacteriophage therapy, which employs bacterial viruses to selectively eliminate pathogenic bacteria, has re-emerged as a promising strategy in the face of increasing antimicrobial resistance. However, its widespread clinical implementation is constrained by concerns regarding safety, standardisation, and predictable efficacy. In this review, we examine the key role of genomics in transforming phage therapy from an empirical practice into a standardised and personalised modality of contemporary m

This paper posits that the most significant long-term existential risk to human civilization is not an acute technological or environmental catastrophe, but a chronic, systemic decay driven by the psychological and demographic consequences of a biologically capped lifespan. The entrenched expectation of mortality before 120 years fosters a condition of "temporal myopia," which cultivates cultural short-termism, consumerist nihilism, and demographic apathy. A critical and compounding aspect of th

Differential privacy (DP) is a prominent technique for protecting sensitive patient data in medical deep learning (DL), yet deploying it without compromising clinical utility or equity remains challenging. This scoping review synthesizes applications of DP in medical DL across centralized and federated settings. A structured search identified 74 eligible studies published through March 2025. Across modalities and tasks, DP, especially via DP-SGD, can maintain clinically acceptable performance un

Abstract Recent advancements in artificial intelligence (AI) have catalyzed the emergence of agentic information systems (IS), which exhibit autonomous behavior and advanced cognitive capabilities. Unlike traditional IS, which functioned primarily as reactive tools supporting humans, agentic IS can make decisions independently, act in unstructured environments, and even delegate tasks to humans. This paradigm shift fundamentally transforms the human-IS relationship, questioning the long-standing

Generative AI (GAI) promises superior analytics and agility in strategy work, yet organisations struggle to move beyond pilots towards routinised decision inputs. This study investigates how GAI becomes embedded in strategic decision-making (SDM) through a qualitative single-case analysis of a global multi-brand group, based on 27 semi-structured executive interviews triangulated with internal documents and industry reports. Structured inductive coding yields a process model identifying enablers

This narrative review synthesizes 2015–2025 evidence on evidence-based production (EBP) of herbal medicines with emphasis on advanced production technologies, omics-enabled authentication, quality by design (QbD), and regulatory harmonization relevant to Indonesia. We map how in vitro root culture, bioreactor scale-up, elicitation/metabolic engineering, and nanotechnology address supply variability and improve consistency; how DNA barcoding/metabarcoding and metabolomics with chemometrics underp

This perspective analyzes the substantial advantages of Artificial Intelligence (AI) and machine learning (ML) in improving the efficacy and precision of biosensors, facilitating accurate detection of diverse physiological signals. Moreover, it emphasizes contemporary developments in biosensor technology and their uses in medical diagnosis, stressing their ability for early disease detection and continuous monitoring. The study also addresses major barriers to more widespread use, such as the la

Advances in artificial intelligence (AI) have made it easier to create highly realistic deepfake videos, which can appear to show someone doing or saying something they did not do or say. Deepfakes may present a threat to individuals and society: for example, deepfakes can be used to influence elections by discrediting political opponents. Psychological research shows that people's ability to detect deepfake videos varies considerably, making us potentially vulnerable to the influence of a video

Background: Automated brain tumor segmentation on multi-parametric magnetic resonance imaging (mpMRI) is crucial in assessing patient outcomes and remains a challenge across Sub-Saharan Africa (SSA). Since 2012, the Brain Tumor Segmentation (BraTS) Challenge has evaluated state-of-the-art artificial intelligence (AI) methods to detect, characterize, and classify tumors. However, it is unclear if these methods can generalize, and hence be widely implemented, in SSA populations. To address this, t

With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes. Considering this rapid technical progress, in this survey, we trace the recent advances of Medical Large Language Models (Med-LLMs), including the background, key findings, and mainstream techniques, especially for the evolution from general-p

Quantum computing is considered to have tremendous potential to help take the emerging field of "Computational Law" to the next level of growth in terms of the expression and implementation of legal principles. With the promise of quantum technology's increasing influence on the legal industry in mind, this essay utilizes the emerging field of Computational Complexity Theory to explore the types of problems that quantum computing is capable of solving more efficiently than classical computing, w

Artificial intelligence (AI) is increasingly embedded in education through adaptive platforms, intelligent tutoring systems, and generative tools. While these technologies promise efficiency and personalization, they also raise concerns about pedagogical deskilling, reduced teacher autonomy, and ethical risks. This paper conceptualizes the potential impacts of AI on teaching expertise and instructional design through the lens of Cognitive Load Theory (CLT). The aim is to conceptualize how AI may

As an important development in CALL, artificial intelligence (AI)-assisted foreign language teaching not only offers unique advantages in leveled reading, vocabulary, pronunciation, self-assessment, personalized testing, and information retrieval but also effectively enhances learner interaction and emotional regulation in reading. Despite these advancements, research on learner emotional engagement in AI-augmented EFL reading instruction remains limited, and there is a lack of sufficient empiri

BACKGROUND: With the continuous advancement of cancer treatment technology, the proportion of cancer survivors is gradually increasing, but they also face many psychological challenges. These challenges can seriously affect their quality of life. Telemedicine, as an innovative medical service model, can be combined with psychosocial intervention to provide cancer survivors with convenient, economical and accessible services to assist them in more effectively managing the difficulties posed by ca

Unified Natural Ethics Theory (UNET) offers a trace-sourced account of why ethical and unethical behavior occur, not merely how such behavior should be judged after the fact. UNET argues that ethics is not fundamentally an externally imposed, mind-dependent, doctrine-dependent, or deity-dependent phenomenon, but a trace-sourced emergent coherence dynamic inherent to informational propagation itself. It frames ethics as an informational, emergent, evolutive, and propagative modulation regime: a c

Detection, classification, and localisation of animal sounds are essential in many ecological studies, including density estimation and behavioural studies. Real-time acoustic processing can also be used in mitigation exercises, with the possibility of curtailing harmful human activities when animals are present. Animal vocalisations vary widely, and there is no single detection algorithm that can robustly detect all sound types. Human-in-the loop analysis is often required to validate algorithm

Integrating artificial intelligence (AI) into higher education can revolutionise traditional learning paradigms by enhancing self-directed learning and fostering student autonomy. This systematic research paper examines the role of AI in supporting these educational shifts, analysing its impact on student engagement, personalised learning experiences and academic performance. Through a comprehensive review of existing literature, this study explores various AI-driven tools and applications that

Abstract As Artificial Intelligence (AI) continues to transform the public sector, particularly in the domain of public deliberation and democratic governance, it also presents distinct challenges. This chapter explores the insights from the ORBIS project, which is situated at the intersection of AI technology and democratic processes. The chapter will critically present and discuss how the project is contributing to advance AI-enhanced democratic and inclusive deliberations by developing AI too