This study aimed to investigate the relationship between pediatric nurses' levels of artificial intelligence literacy and artificial intelligence anxiety. The study population consisted of 246 nurses working at children hospital within a city hospital in Ankara, Türkiye. Data were collected using a researcher-developed Participant Information Form, the Artificial Intelligence Literacy Scale and the Artificial Intelligence Anxiety Scale. Statistical methods included descriptive statistics, independent samples t-test, ANOVA, Mann-Whitney U test, Kruskal-Wallis H test, and Spearman correlation, Multiple linear regression analysis. Statistical significance was accepted as p < 0.05. Pediatric nurses' artificial intelligence literacy point was measured as 58.94 ± 10.36, and their artificial intelligence anxiety point was measured as 43.05 ± 13.29. Demographic factors significantly influenced outcomes: single nurses and those with higher education exhibited greater AI literacy, while older nurses (≥30 years) reported higher anxiety. Nurses who viewed as facilitative for care demonstrated higher AI literacy, whereas those perceiving as a job threat showed lower literacy and higher AI anxiety. A weak negative correlation indicated that higher AI literacy was associated with reduced anxiety, particularly in learning-related and job-displacement concerns. To prepare pediatric nurses for the digital transformation of healthcare services, it is recommended that institutions prioritize educational programs focused on artificial intelligence literacy alongside the establishment of robust institutional infrastructure and technical support mechanisms. Enhancing AI literacy among pediatric nurses may contribute to lowering their AI-related anxiety and promoting the more effective integration of AI technologies into pediatric nursing care.

Digital technologies and artificial intelligence are rapidly transforming emergency nursing, raising concerns about how nurses can sustain holistic, person-centered care in highly technologized environments. Organizational conditions such as the clinical governance climate, along with nurses' psychological and technological capacities, may play a decisive role in shaping holistic nursing competence in digitally evolving emergency care environments; however, their combined influence remains understudied. This study examined how clinical governance climate, digital empathy, and artificial intelligence attitude interact to shape holistic nursing competence among emergency nurses. A cross-sectional study was conducted among 443 emergency nurses from 8 hospitals in Egypt. Data were collected over a 3-month period in 2025 using validated instruments measuring clinical governance climate, digital empathy, artificial intelligence attitude, and holistic nursing competence. Descriptive and correlational analyses were performed, and a moderated mediation model using the PROCESS model 14 was used to test conditional effects. Clinical governance climate significantly predicted digital empathy and holistic nursing competence. Digital empathy strongly predicted holistic nursing competence (unstandardized coefficient = 0.33; P<.001). Artificial intelligence attitude had a significant direct effect on holistic nursing competence (unstandardized coefficient = 0.28; P<.001) and moderated the relationship between digital empathy and holistic nursing competence (interaction unstandardized coefficient = 0.32; P<.001). Digital empathy mediated the relationship between clinical governance climate and holistic nursing competence (indirect unstandardized coefficient = 0.15; P<.001), with a stronger indirect effect among nurses reporting more favorable attitudes toward artificial intelligence. Supportive clinical governance strengthens holistic nursing competence directly and through enhanced digital empathy. Positive artificial intelligence attitudes further amplify nurses' capacity to translate empathy into holistic care, underscoring the need for governance-driven support, empathy training, and artificial intelligence-readiness strategies in emergency nursing.

The implementation of artificial intelligence has been investigated in many aspects of cardiovascular disease. To develop deep learning models based on coronary angiograms to detect functionally significant coronary stenoses. A total of 610 frames from 122 coronary arteries that received pressure wire-based fractional flow reserve (FFR) assessment were analyzed. Deep learning models were developed for the segmentation and classification of coronary stenoses. Both internal and external validation of the deep learning models were performed. The mean FFR value was 0.84 ± 0.08. The artificial intelligence-based FFR was significantly correlated with wire-based FFR with an average correlation coefficient of 0.68 and a mean absolute error of 0.05. The diagnostic performance of artificial intelligence-based FFR versus wire-based FFR was accuracy 87.6%, F1 score = 83.6%, and recall = 81.1%. The artificial intelligence-based FFR showed good discriminative performance with an area under the receiver operating characteristic curve of 86.5% (95% CI: 79.3-93.6). The artificial intelligence-based FFR showed moderate agreement with pressure wire-based FFR and showed promising diagnostic performance in the internal cohort, although reduced performance was observed in external validation, warranting further refinement and multicenter validation.

Artificial intelligence shows promise for improving care for peripheral artery disease through earlier detection, improved risk stratification, more tailored treatment planning, and more efficient care delivery. This American Heart Association science advisory reviews the current and emerging applications of artificial intelligence across the peripheral artery disease care continuum, including population-level screening, imaging diagnostics, outcome prediction, aneurysm risk estimation, and surgical planning. Machine learning and deep learning models demonstrate strong performance in automating peripheral artery disease detection from structured and unstructured electronic health record data, predicting major adverse cardiovascular and limb events, and enhancing diagnostic accuracy through advanced imaging analysis. Multimodal models that integrate clinical, genetic, behavioral, and biomarker data further enhance predictive precision and support individualized care strategies. Despite these advancements, real-world implementation of artificial intelligence in peripheral artery disease remains limited because of challenges in clinician training, regulatory clarity, data governance, and equitable access. We outline key barriers to adoption and propose strategies to address professional, legal, and ethical concerns, including mitigating bias and leveraging implementation frameworks. Ensuring the trustworthy, fair, and effective integration of artificial intelligence into vascular care will require interdisciplinary collaboration, ongoing validation, and robust oversight. This science advisory serves as a guide for clinicians, researchers, and policymakers on the responsible deployment of artificial intelligence in the diagnosis and management of peripheral artery disease.

<jats:p>Ang pangunahing layunin ng pag-aaral na ito ay upang malaman ang ugnayan ng impluwensya ng artificial intelligence at kaligirang pampagkatuto sa akademikong perfromans ng mga mag-aaral. Kung kaya’t ang pag-aaral na ito ay may layuning siyasatin ang makabuluhang ugnayan sa pagitan ng impluwensya ng artificial intelligence at kaligirang pampagkatuto sa akademikong performans ng mga mag-aaral sa unang taon ng BSED Filipino ng KCAST. Nagtamo ang mga layunin ng pag-aaral na ito sa pamamagitan ng estadistikong gamit. Ang mga kalahok na pag-aaral ay binubuo ng 117 ng mag-aaral sa unang taon ng BSED Filipino. Ayon sa resulta nakakuha ng mataas na antas sa nakikitang panganib, nakikitang pagganap, nakikitang pagsisikap, kalagayan ng pasilidad, pag-uugali, gustong pag-uugali, at adaptsyon ng AI sa mataas na edukasyon sa artificial intelligence. Pagdating sa kaligirang pampagkatuto ang tiyak na positibong pakikitungo sa loob ng silid-aralan, pagkakaiba-iba ng paniniwala at pag-uugali, pansariling pagsalungat at pagtitiyaga sa katayuan ay nakakuha ng mataas. Sa pagdating sa akademikong performans na kakayahan, ugali, at kilos ay nakakuha ng mataas. Samakatuwid, ang resulta ay nangangahulugang ang null hypothesis ng pag-aaral ay tinanggap sapagkat malinaw na ang tatlong baryabol ng pag-aaral na ito ay walang makabuluhang ugnayan. Ibig sabihin nito na ang indikasyon na nagpapatunay na nararapat pagtuunan ng pansin ang pagpapaunland ng artificial intelligence upang umunlad ang kaligirang pampagkatuto at umunlad rin ang akademikong perfromans ng mga mag-aaral. MGA SUSING SALITA: artificial intelligence, kaligirang pampagkatuto, akademikong perfromans, at mag-aaral.</jats:p>

Scholars, stakeholders, and the government have given significant attention to the development of renewable energy in recent times. However, previous research has failed to acknowledge the potential impact of artificial intelligence on advancing renewable energy development. Drawing insights from a global dataset encompassing 63 countries over the period 2000 – 2019, this paper provides significant observations regarding the influence of artificial intelligence on the progress of renewable energy, by using the Instrumental Variable Generalized Method of Moments model. We also explore their asymmetric nexus, and the potential mediation effect. Moreover, this study explores the moderating role of climate finance and highlights the following interesting findings. First, artificial intelligence contributes significantly to the enhanced development of renewable energy, and this primary finding holds after two robustness tests of changing independent and dependent variables. Second, artificial intelligence has an asymmetric effect on renewable energy development, and their nexus is closer in countries with lower levels of renewable energy development. Thid, artificial intelligence works on renewable energy development through technology effect and innovation effect. Fourth, climate finance also presents direct benefits to renewable energy development; simultaneously, climate finance plays an effective moderating role in the relationship between artificial intelligence and renewable energy development. These findings inspire us to propose policy implications to promote the enhanced development of renewable energy.

Games have been the perfect test-beds for artificial intelligence research for the characteristics that widely exist in real-world scenarios. Learning and optimisation, decision making in dynamic and uncertain environments, game theory, planning and scheduling, design and education are common research areas shared between games and real-world problems. Numerous open-source games or game-based environments have been implemented for studying artificial intelligence. In addition to single- or multi-player, collaborative or adversarial games, there has also been growing interest in implementing platforms for creative design in recent years. Those platforms provide ideal benchmarks for exploring and comparing artificial intelligence ideas and techniques. This paper reviews the games and game-based platforms for artificial intelligence research, provides guidance on matching particular types of artificial intelligence with suitable games for testing and matching particular needs in games with suitable artificial intelligence techniques, discusses the research trend induced by the evolution of those games and platforms, and gives an outlook.

This article reviews the "Once learning" mechanism that was proposed 23 years ago and the subsequent successes of "One-shot learning" in image classification and "You Only Look Once - YOLO" in objective detection. Analyzing the current development of Artificial Intelligence (AI), the proposal is that AI should be clearly divided into the following categories: Artificial Human Intelligence (AHI), Artificial Machine Intelligence (AMI), and Artificial Biological Intelligence (ABI), which will also be the main directions of theory and application development for AI. As a watershed for the branches of AI, some classification standards and methods are discussed: 1) Human-oriented, machine-oriented, and biological-oriented AI R&D; 2) Information input processed by Dimensionality-up or Dimensionality-reduction; 3) The use of one/few or large samples for knowledge learning.

There has been a recent resurgence in the area of explainable artificial intelligence as researchers and practitioners seek to make their algorithms more understandable. Much of this research is focused on explicitly explaining decisions or actions to a human observer, and it should not be controversial to say that looking at how humans explain to each other can serve as a useful starting point for explanation in artificial intelligence. However, it is fair to say that most work in explainable artificial intelligence uses only the researchers' intuition of what constitutes a `good' explanation. There exists vast and valuable bodies of research in philosophy, psychology, and cognitive science of how people define, generate, select, evaluate, and present explanations, which argues that people employ certain cognitive biases and social expectations towards the explanation process. This paper argues that the field of explainable artificial intelligence should build on this existing research, and reviews relevant papers from philosophy, cognitive psychology/science, and social psychology, which study these topics. It draws out some important findings, and discusses ways that these can be infused with work on explainable artificial intelligence.

The advent of artificial intelligence has changed many disciplines such as engineering, social science and economics. Artificial intelligence is a computational technique which is inspired by natural intelligence such as the swarming of birds, the working of the brain and the pathfinding of the ants. These techniques have impact on economic theories. This book studies the impact of artificial intelligence on economic theories, a subject that has not been extensively studied. The theories that are considered are: demand and supply, asymmetrical information, pricing, rational choice, rational expectation, game theory, efficient market hypotheses, mechanism design, prospect, bounded rationality, portfolio theory, rational counterfactual and causality. The benefit of this book is that it evaluates existing theories of economics and update them based on the developments in artificial intelligence field.