Mental health challenges add immensely to the global burden of disease, yet traditional approaches to psychological assessment and care remain resource intensive and often inaccessible. There is widespread interest in testing whether advances in artificial intelligence (AI), particularly large language models (LLMs), could address these constraints. This review focuses on LLMs, given the field's explosive interest in testing whether their ability to generate context-sensitive language representations can aid large-scale assessment and intervention. We synthesize recent applications of LLMs, including language-based assessment of psychopathology, digital phenotyping, electronic health record analysis, and early integrations into psychotherapy. However, we highlight deep challenges of AI that loom large in the highly sensitive space of mental health treatment, including clear risks of bias, hallucinations, inappropriate (or even dangerous) therapeutic recommendations, and limited regulatory oversight. We conclude with future directions that are critical for the safe and equitable use of LLMs in mental health.

The application of artificial intelligence (AI) is increasingly valuable as a tool and assistant in many areas of clinical and academic medicine. Generative AI (GenAI) creates new content used by large language models, which can generate language that strongly resembles or even improves on that of humans. Learners and educators in many areas of education are using GenAI for essays and assessments, raising issues regarding learning and assessment. GenAI is also raising new concerns in health professions education (HPE), an area of health professions training that sometimes has different aims and assessment methods compared to its clinical counterparts. HPE needs to assess levels of knowledge and understanding of pedagogy, and the use of GenAI presents challenges to its current assessments, which are predominantly written. The study aimed to investigate educators' and learners' perspectives on the opportunities and challenges presented by GenAI in postgraduate HPE assessments. It particularly focused on perspectives of how GenAI may influence the future of assessment and essay-based assessments in HPE. Informed by a constructivist paradigm, a qualitative approach was adopted, undertaking 8 semistructured interviews conducted via Microsoft Teams. Purposive sampling ensured a mixture of educators and learners in current HPE courses from a range of health care professions. Data were thematically analyzed. There was no difference between educator and learner perspectives. Four themes were identified: AI is here, students are at a disservice if we do not embrace it; AI as an opportunity to rethink HPE assessments; AI is a "gray area"; and AI is fallible. The findings present AI as an external catalyst, highlighting the current internal desire for assessment change within HPE. It offers opportunities for creative, authentic assessments that reflect real-life academic and clinical practice, aiming to develop competent future HPE educators and keep courses relevant. These findings contribute to the debate around the future potential and development of AI in HPE assessments.

The intensifying global competition among cities necessitates accurate and efficient evaluations of urban business environments to drive economic growth, attract investment, and foster innovation. Traditional assessment methods, often reliant on expert opinions and manual analysis, are prone to subjectivity and inefficiency. To address these limitations, this study introduces an optimized Siamese Neural Network model designed to improve the accuracy and efficiency of urban business environment evaluations. The model leverages feature extraction and multidimensional learning to analyze key indicators, including economic development, infrastructure integrity, policy friendliness, and market entry difficulty, utilizing publicly available datasets. Additionally, the model incorporates emerging technologies, including the Internet of Behaviors and generative artificial intelligence (AI), to bolster capabilities in capturing and analyzing complex behavioral data. The Internet of Behaviors enables the collection of real-time dynamic behavioral data from various urban activities, providing a comprehensive and detailed understanding of the business environment. Generative AI, on the other hand, generates predictive models from existing data, simulating future trends and scenarios, thereby enhancing the accuracy and foresight of decision-making. Performance comparison experiments demonstrate the model's superiority over baseline models across all evaluation metrics. Specifically, the optimized model achieves F1 Scores of 0.874, 0.879, and 0.882 on the Doing Business Indicators, Urban Land Cover Classification, and Open Cities Artificial Intelligence Challenge datasets, respectively, significantly outperforming the Graph Neural Network for Business Environment and Transformer-based Business Environment Evaluation models. Furthermore, the model exhibits exceptional efficiency, with training times of 29.648s, 31.327s, and 32.843s on the respective datasets. In terms of scalability and adaptability, the model achieves Scalability Scores and Generalization Capabilities of 0.821 and 0.876 on the DBI dataset, demonstrating its effectiveness in handling large-scale, multidimensional data. A comprehensive evaluation of urban business environments revealed specific strengths and weaknesses in cities A, B, and C. City A excelled in economic development (8.5) and infrastructure integrity (9.0) but scored lower in market entry difficulty (5.5). City B showed balanced performance across all metrics, while City C demonstrated strengths in policy friendliness (8.5) and market entry difficulty (8.0) but lower scores in infrastructure integrity (6.5). These results highlight the model's utility in identifying areas for improvement and fostering targeted interventions. This study advances the theoretical and practical application of deep learning techniques in urban business environment evaluation, offering city administrators an efficient and objective decision-support tool. By enabling data-driven policy formulation and resource optimization, the proposed model provides a robust strategy for enhancing urban competitiveness.

Artificial intelligence (AI) and big data are increasingly applied in drug regulation and have demonstrated significant potential worldwide. The U.S. Food and Drug Administration (FDA) has developed a relatively comprehensive approach through strategic frameworks, regulatory guidelines, and pilot programs. The European Medicines Agency (EMA) has promoted AI adoption via the Big Data Task Force, DARWIN EU®, and a multi-annual work plan, while Japan, Canada, and other countries have also advanced relevant initiatives and strengthened international cooperation. In China, smart regulation has been incorporated into the "14th Five-Year Plan" and subsequent strategies, with progress in establishing national regulatory data platforms, pharmaceutical traceability systems, and pilot AI applications. Nevertheless, AI in drug regulation remains at an exploratory stage, facing challenges such as limited model reliability and interpretability, insufficient data standards and interoperability, regulatory gaps, and ethical as well as public trust concerns. Future progress will depend on strengthening regulatory standards, enhancing data governance, improving regulatory capacity, and deepening international collaboration to achieve more scientific, intelligent, and efficient drug regulation.

From spatial maps to therapeutic targets: Next challenge for artificial intelligence in cancer spatial omics.

The integration of robotics and artificial intelligence (AI) has significantly transformed modern industries by enhancing automation, efficiency and decision-making capabilities. Robotics, combined with advanced AI techniques such as machine learning and deep learning, enables systems to perform complex tasks with greater precision and adaptability. This study reviews the current developments, applications and challenges of robotics and AI across various sectors including manufacturing, healthcare, logistics and education. It highlights the role of AI-powered robots in improving productivity, reducing human error and enabling human-robot collaboration. The paper also discusses key challenges such as safety, security, ethical concerns and the need for advanced algorithms. Furthermore, it examines the evolution of AI from its early stages to the present era of deep learning and big data. The findings suggest that while robotics and AI offer immense potential for societal and industrial advancement, responsible development, ethical frameworks and regulatory measures are essential to ensure safe and beneficial implementation in the future.Keywords: Robotics, Artificial Intelligence (AI), Machine Learning, Deep Learning, Automation, Human-Robot Interaction, Industrial Applications, Healthcare Robotics, Ethical Issues in AI, Intelligent Systems.

Abstract Abstract — The automatic generation of visual scenes from natural language descriptions represents a frontier challenge in artificial intelligence, bridging the gap between linguistic understanding and photorealistic visual synthesis. This paper presents a comprehensive two-phase framework for Text Scene Synthesis. In Phase 1, we leverage a state-of-the-art denoising diffusion probabilistic model (DDPM) conditioned on text embeddings to produce high-fidelity, semantically accurate images from arbitrary textual prompts. In Phase 2, we extend the framework to temporal visual synthesis by employing Contrastive Language-Image Pretraining (CLIP) alongside a video generation pipeline to produce coherent short-form video sequences directly from text. Experimental evaluation demonstrates that the proposed system significantly outperforms prior GAN-based, VAE-based, and standalone diffusion approaches in terms of image fidelity, semantic consistency, and temporal coherence. The system achieves a Fréchet Inception Distance (FID) of 12.4 on COCO-captions and a CLIPSIM score of 0.312 for video generation, representing meaningful improvements over prior methods. This work demonstrates the promise of unified text-to-scene pipelines for applications spanning entertainment, education, game development, and assistive technologies. Keywords — text-to-image synthesis, diffusion models, CLIP, video generation, scene synthesis, generative AI, denoising diffusion probabilistic model, natural language processing.

This quantitative study investigates the utilization and challenges of Artificial Intelligence (AI) in selected Higher Educational Institutions (HEIs) in Northeastern Thailand, aiming to inform the development of an institutional AI policy framework. The demographic profile revealed a workforce predominantly composed of young to middle-aged professionals, with a majority holding postgraduate degrees. AI utilization remained moderate across four core academic domains: research (M=2.45), extension (M=2.41), instruction (M=2.38), and production (M=2.24). Key challenges received an overall "very serious" rating (M=2.53). Gender and marital status showed significant positive correlations with AI utilization, while permanent employment and higher education levels correlated negatively in certain domains. Seminar frequency was a strong predictor of higher AI use. Findings underscore the need for targeted faculty development, resource support, and robust institutional policies.

The integration of Artificial Intelligence (AI) in academic libraries globally has catalyzed significant transformation in library and information services. This paper provides a comprehensive review of the application, prospects, requirements, and challenges of AI in tertiary institution libraries, with special reference to Jigawa State, Nigeria. The state’s libraries, while showing slow but perceptible signs of AI adoption, face numerous challenges ranging from infrastructural inadequacies, funding gaps, digital skills shortages, and policy limitations. The paper reviews current literature, highlights successful case studies, analyzes implementation gaps, and offers actionable recommendations for policymakers, educational leaders, and library professionals. The findings highlight that with deliberate investment, capacity building, and coherent policy frameworks, Jigawa libraries can harness the full potential of AI to support enhanced research, teaching, and learning.

Hand hygiene is a fundamental measure for preventing healthcare-associated infections, yet traditional monitoring methods are significantly limited by the Hawthorne effect, high resource demands, and an inability to assess procedural quality. Artificial intelligence (AI) technology has emerged as a transformative, automated, and objective approach to address these long-standing challenges. This scoping review sought to systematically map the existing evidence, technical pathways, performance metrics, and implementation challenges of AI for monitoring hand hygiene compliance in healthcare settings. Following the Joanna Briggs Institute (JBI) methodological framework and PRISMA-ScR guidelines, we searched five major databases (PubMed, Scopus, Embase, Web of Science, and IEEE Xplore) for articles published between January 2000 and September 2025, supplemented by grey literature searching and backward citation tracking. Two reviewers independently screened records, assessed full-text reports for eligibility, and extracted data, which were synthesized using descriptive statistics and thematic analysis. Of 800 records identified through database and supplementary searches, 45 studies (2007-2025) were included. The primary technical pathways identified were computer vision (53.3%), wearable sensors (24.4%), Internet of Things-integrated systems (13.3%), and radar/radio frequency-based systems (8.9%). While computer vision achieved high accuracy (95%) in setting-specific ICU models, performance dropped to 56% in generalizable models. Wearable systems demonstrated portability but showed 5%-10% lower specificity than vision-based approaches. Most evidence is derived from small-scale technical validations, with a significant lack of formal fairness analysis and evaluation of clinical workflows or cost-effectiveness. AI-based hand hygiene monitoring shows promise for supporting more objective and scalable hand hygiene surveillance in healthcare settings. However, the field remains at a largely pre-translational stage. Future research should shift from technical feasibility toward implementation science, focusing on establishing standardized motion databases, evaluating ethical governance (e.g., privacy and automation bias), and conducting pragmatic trials to demonstrate sustained clinical benefit and organizational sustainability.

Ethical Challenges of Artificial Intelligence in Human Resource Management

This study closely examines the perceptions of Saudi EFL learners regarding the inclusion of AI tools in language classrooms. The study included 123 EFL learners from diverse backgrounds, including gender, age, educational level, experience using AI, and region. A questionnaire was used to gather the data which indicated that with the exception of years of exposure to higher education, the participants’ perception show commonality across all other variables. The study gathered data on four constructs (Perceived Usefulness of AI in English Learning, Learner Attitudes and Acceptance Toward AI Use, Technical and pedagogical challenges, and Psychological and Ethical concerns). Responses were analyzed for statistical significance, which indicated that Saudi EFL learners generally possess positive views about the efficacy and use of AI in language learning, particularly in improving grammar, vocabulary, pronunciation, and providing written feedback. In addition, they shared the view that AI is a constructive agent in helping them not only improve their language proficiency but it also accelerates the learning process thus providing better learning opportunities irrespective of individual differences. At the same time, they have reservations about the outcomes when human agency is replaced by AI, indeed they firmly believe that AI can at best complement rather than replace the teacher in classrooms. The study concludes with pertinent recommendations.

With the rapid development of generative artificial intelligence, AI-driven content generation tools are deeply integrated into the creative industry. This paper systematically discusses the application status and potential challenges of artificial intelligence in creative industries. It is found that AI has significantly improved the efficiency of content production, but there are still limitations in emotional expression, cultural context understanding, and originality. At the same time, its wide application has also caused ethical and structural problems such as copyright ownership and creative homogenization. This paper further proposes that man-machine collaboration should be the core paradigm of creative production in the future and calls for the establishment of a governance framework that takes into account technological innovation and humanistic values. These insights offer practical guidance for policymakers, industry practitioners, and scholars seeking to navigate the complex integration of AI within the creative industries.

The use of artificial intelligence (AI) in pediatric musculoskeletal imaging has undergone significant expansion over the past few years. Until recently, the use of AI was limited to evaluating bone age and opportunistically assessing the bone health index. Currently, validated AI software for commercial use includes the detection of appendicular fractures, automated measurement of scoliosis, assessment of lower limb length discrepancy, and assessment of developing hip dysplasia. For other applications, further work is needed. Diagnostic accuracy for detecting rib and vertebral fractures in children using AI is currently not satisfactory; however, future research using enhanced deep learning is projected to address these limitations. The implementation of other applications of diagnostic AI in pediatric musculoskeletal imaging for non-accidental trauma, bone dysplasia, and tumor assessment is hindered by the lack of large pediatric datasets, which would require multicenter collaborations. This paper aims to succinctly outline the present clinical applications of AI in the pediatric musculoskeletal field, while elucidating existing possibilities, limitations, and future needs and prospects.

Chronic diseases—including cardiovascular disease, cancer, chronic obstructive pulmonary disease, diabetes, and depression—continue to rise globally, largely driven by modifiable behavioural risk factors. An estimated 40% of premature deaths are attributable to preventable behaviours such as smoking, unhealthy diets, and physical inactivity.¹ Preventive healthcare aims to identify risk early and intervene before disease onset or progression, thereby reducing morbidity, mortality, and costs. Artificial intelligence (AI) is increasingly positioned as a scalable enabler of preventive care via predictive analytics, automated risk stratification, conversational agents, and digital therapeutics. This narrative review synthesises the opportunities and challenges of AI in preventive healthcare, with illustrative examples of real-world tools (Ada Health, Lark Health Coach AI, GECA, Dejal@bot, and CoachAI). We highlight AI’s promise for earlier detection, faster and more consistent decision support, and enhanced outreach and behaviour change interventions, while critically examining barriers including non-representative data, bias, calibration and generalisability limitations, privacy and security risks, and the “black-box” problem that undermines clinical trust. Responsible integration of AI into preventive care requires robust governance, transparent evaluation, equity-oriented data strategies, and clinician oversight to ensure safety, effectiveness, and public confidence.

Integrating Artificial Intelligence (AI) into Islamic education represents a significant advancement in educational innovation, offering new possibilities for enhancing learning experiences, expanding access, and supporting curriculum development. However, its implementation must be approached with caution to ensure alignment with Islamic ethical principles, educational traditions, and cultural diversity. This qualitative, literature-based study systematically analyzes peer-reviewed articles, reports, and books published between 2019 and 2024 to explore the potentials and challenges of AI in Islamic education. The study focuses on three critical aspects: (1) the development of AI technologies that comply with Islamic values and shariah, (2) teacher training for value-conscious and effective AI use, and (3) sustainability strategies for long-term integration. Findings suggest that while AI offers promising solutions for personalization and accessibility, its effectiveness depends on ethical oversight, technological infrastructure, and pedagogical readiness. This study proposes a conceptual framework to guide responsible AI integration in Islamic education, contributing to developing future-ready and values-driven educational models.

The AI risk repository: A meta-review, database, and taxonomy of risks from artificial intelligence

With the rapid integration of artificial intelligence (AI) into educational practice, its influence on teachers’ innovative teaching behavior has attracted growing attention. However, less is known about the dual psychological mechanisms through which AI use may both facilitate and constrain teaching innovation. Drawing on the cognitive appraisal theory of stress, this study examined whether teachers’ challenge and threat appraisals mediate the relationship between AI use and innovative teaching behavior, and whether school innovation support moderates these pathways. A nationwide survey was conducted among 1275 primary and secondary school teachers in China. Data were collected using validated measures of AI use, challenge appraisal, threat appraisal, innovative teaching behavior, and school innovation support, and were analyzed through confirmatory factor analysis, structural equation modeling, and bootstrapped moderated mediation analysis. The results indicated that AI use was positively associated with both challenge appraisal and threat appraisal. In turn, challenge appraisal was positively associated with innovative teaching behavior, whereas threat appraisal was negatively associated with it. Challenge and threat appraisals both served as significant mediating pathways linking AI use to innovative teaching behavior. In addition, school innovation support moderated the effects of AI use on both appraisals: higher levels of support strengthened the positive association between AI use and challenge appraisal, but unexpectedly also amplified its association with threat appraisal. These findings highlight the double-edged nature of AI use in educational settings and suggest that cognitive appraisal is an important mechanism through which AI use relates to teachers’ innovative teaching behavior. The study further implies that schools should provide supportive conditions that encourage challenge appraisal while carefully managing the pressures that may intensify threat appraisal.

This article examines the opportunities and challenges of artificial intelligence (AI) in predictive policing and criminal justice, with a comparative analysis of applications in France, the UAE, and the UK. These technologies span crime prevention, evidence collection, and judicial decision-making. Adopting a comparative theoretical legal approach, the study analyzes relevant legal frameworks to determine their adequacy. While AI enhances law enforcement efficiency, it raises significant concerns regarding privacy, procedural justice, and evidence integrity. The findings emphasize that integrating AI into justice systems requires precise legal regulation to balance technological innovation with constitutional rights. The study concludes that establishing clear definitions for predictive justice and ensuring the legitimacy of AI-generated evidence are essential. Furthermore, AI integration must adhere to principles of transparency and accountability, supported by continuous auditing and judicial oversight. Ultimately, this paper proposes a normative analytical framework that aligns AI innovation with fundamental rights and the rule of law.

Artificial intelligence (AI) is transforming the medical field, including pediatric surgery, by enhancing diagnostic precision, surgical planning, intraoperative guidance, and postoperative care. Despite the increasing interest, a comprehensive understanding of AI's current applications and challenges in pediatric surgery is lacking. This narrative review aims to summarize current AI applications in pediatric surgery and evaluate their impact on clinical care, surgical training, and patient outcomes. A literature review was conducted in January 2025 using PubMed and Web of Science. The search terms were "artificial intelligence" and "pediatric surgery", restricted to English-language articles published from 2015 to 2025. After excluding studies on adult populations and unrelated specialties, 46 articles were selected for analysis. The reviewed literature demonstrates AI's utility across the pediatric surgical spectrum. Most prominently, AI enhances diagnostic accuracy for conditions like appendicitis, necrotizing enterocolitis (NEC), Hirschsprung's disease (HD) and biliary atresia. AI also supports surgical planning via three-dimensional (3D) modeling, augments intraoperative navigation and skill assessment, and improves postoperative care through complication prediction and pain evaluation. Furthermore, AI tools facilitate caregiver education and emotional support. Nevertheless, limitations remain, including pediatric data scarcity, variability in model reporting, and unresolved ethical and legal concerns. AI holds great potential for transforming pediatric surgical care by improving accuracy, efficiency, and personalization. Future research should focus on generating robust pediatric datasets, validating tools in clinical settings, and integrating ethical frameworks. AI-human collaboration could reshape pediatric surgery and contribute meaningfully to patient care and policy development.