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2026 xu2026impact DATABASE
Impact of Hospital Hierarchy on Nurses' Attitudes Toward Artificial Intelligence: Mediating Roles of Artificial Intelligence Literacy and Anxiety.

Xu, Ding; Zheng, Yimeng; Yin, Yongtian; Lin, Cuixia; Yang, Linlin; Liang, Beibei; Du, Jielong

Journal of Nursing Management , 2026 : e5593996

With the rapid development of science and technology, the application of artificial intelligence in the field of healthcare is becoming increasingly widespread. As the executors and responsible persons of nursing work, nurses' understanding and attitude toward AI technology determine whether AI can be deeply integrated and successfully applied in the field of nursing. To investigate nurses' negative and positive attitudes toward the use of artificial intelligence and influencing factors, explore the mediating effect of artificial intelligence literacy and anxiety between hospital hierarchy differences and negative and positive attitudes toward the use of artificial intelligence, and provide basis for improving nurses' attitudes toward the use of artificial intelligence. In November 2025, the convenience sample of 436 nurses from different hospitals in Shandong Province was surveyed. Data were collected using the general information questionnaire, the attitude scale toward the use of artificial intelligence technologies in nursing, the artificial intelligence anxiety scale, and the artificial intelligence literacy scale. Multiple linear regression analyzed the influencing factors of nurses' negative and positive attitudes toward the use of artificial intelligence. Mediation analyses explored the mediating effect of artificial intelligence literacy and anxiety between the hospital hierarchy differences of nurses and their negative and positive attitudes toward the use of artificial intelligence. The score of negative attitude was 14.55 ± 6.63, and the score of positive attitude was 38.21 ± 3.87. Artificial intelligence literacy and anxiety partially mediated the relationship between hospital hierarchy differences and the negative and positive attitudes toward the use of artificial intelligence, with the total mediating effects being 3.067 and -1.011, respectively. Hospital hierarchy differences could directly positively predict the negative and positive attitudes toward the use of artificial intelligence and could also indirectly positively predict the negative attitude toward the use of artificial intelligence through mediation by artificial intelligence literacy and anxiety and negatively predict the positive attitude toward the use of artificial intelligence. Providing personalized artificial intelligence training based on the needs of hospitals could improve nurses' attitudes toward the use of artificial intelligence, increase their artificial intelligence literacy, and reduce their anxiety.
2026 puranik2026comparative DATABASE
Comparative Efficacy of Trained Versus Untrained Generative Artificial Intelligence Platforms in Providing Case-Based Multiple-Choice Questions on Traumatic Dental Injuries in the Pediatric Dentistry Curriculum: A Cross-Sectional Study.

Puranik, Chaitanya P; Turley, Jakob; Pickett-Nairne, Kaci; Katebzadeh, Shahbaz

Dental traumatology : official publication of International Association for Dental Traumatology

To determine the comparative efficacy of trained versus untrained generative artificial intelligence platforms in providing multiple-choice questions on traumatic dental injuries in a pediatric dentistry curriculum. In this cross-sectional study, a standardized prompt was used on three generative artificial intelligence platforms, accessed via web interfaces in 2025 (United States), to generate case-based multiple-choice questions on four domains: avulsion, crown-root fractures, primary teeth, and permanent teeth injuries. The three generative artificial intelligence platforms used were: ScholarGPT, untrained, and trained ChatGPT4o (OpenAI). Evidence-based guidelines from the International Association for Dental Traumatology were used to train the platform. The generative artificial intelligence platforms were asked to select one correct answer from four choices and provide a rationale for the selection. Two calibrated, masked, board-certified pediatric dentists scored the case-based multiple-choice questions using a validated Artificial Intelligence Study Material Assessment and Reliability tool and noted subjective responses for the questions. Statistical analyses were performed with an alpha value of 0.05. All three generative artificial intelligence platforms demonstrated no statistically significant difference (p > 0.05) in terms of their Artificial Intelligence Study Material Assessment and Reliability scores. Some questions had incomplete clinical information (37%), while the options were simplistic (56%) with incorrect rationale (31%). Newer or trained generative artificial intelligence platforms have scores similar to those of untrained platforms, suggesting that publicly available evidence-based information from the International Association for Dental Traumatology enabled the platforms to access these resources for enhanced accuracy. The newer or trained generative artificial intelligence platforms show potential to augment dental trauma education within pediatric dentistry through the development of case-based multiple-choice questions; however, limitations in rationale accuracy and answer quality highlight the need for expert oversight.
2026 uludaşdemir2026the DATABASE
The relationship between artificial intelligence literacy and artificial intelligence anxiety: A cross-sectional study among pediatric nurses.

Uludaşdemir, Dilek; Ayar, Ganime; Yetkin, Eda Emine

Journal of pediatric nursing , 88 : 664-670

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.
2026 el-sayed2026strengthening DATABASE
Strengthening Holistic Nursing Competence Among Emergency Nurses in the Digital Era: The Roles of Clinical Governance, Digital Empathy, and Artificial Intelligence Attitude.

El-Sayed, Ahmed Abdelwahab Ibrahim; Alsenany, Samira Ahmed; Younes, Boshra Mostafa; Hawash, Mervat Abdel Hamid; Asal, Maha Gamal Ramadan

Journal of emergency nursing

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.
2026 jeon2026evaluating DATABASE
Evaluating the Performance of Artificial Intelligence in Accurately Detecting Skin Cancer: An Umbrella Review of Systematic Reviews and Meta-analyses.

Jeon, Jae Joon; Chun, Hayoon; Lee, Judith; Son, Hyunsoo; Lee, Changyoon; Lee, Keeheon; Oh, Sarah Soyeon; Hwang, Shinwon; Hyun, Chul S; Kim, Myung Ha; Cho, Eunyoung; Lee, Solam; Shin, Jae Il

American journal of clinical dermatology

Artificial intelligence technology is being widely developed in dermatology. However, there remains a lack of comprehensive data analyzing the diagnostic performance of artificial intelligence in skin cancer. We aimed to evaluate the diagnostic accuracy of artificial intelligence in skin cancer detection. MEDLINE, Embase, Cochrane library, Web of Science, and Scopus were searched from database inception to 9 April, 2025. Studies were included if they exclusively assessed the diagnostic accuracy of artificial intelligence for primary cutaneous malignancies. The artificial intelligence performance in skin cancer diagnosis was evaluated using accuracy, area under the curve value, sensitivity, and specificity. Twenty-eight systematic reviews and meta-analyses were included. Across the studies, reported sensitivity ranged from 83.7 to 94.4% for basal cell carcinoma, 57.0-90.1% for squamous cell carcinoma, and 48-100% for melanoma. Specificity ranged from 77.9 to 96% for basal cell carcinoma, 92.6-98% for squamous cell carcinoma, and 36-100% for melanoma. Area under the curve values extracted from the reviews varied widely, generally ranged from 0.61 to 0.99. Narrative comparisons within the included studies suggested that deep learning models frequently demonstrated diagnostic performance non-inferior or superior to human clinicians, although prospective validation in real-world clinical workflows remains limited. Current evidence suggests that artificial intelligence technologies have demonstrated potential for skin cancer diagnosis, but with important limitations. Variability in diagnostic metrics, driven largely by data heterogeneity and differing validation strategies, poses significant challenges. Emerging evidence suggests future research should transition toward multimodal artificial intelligence systems that integrate structured clinical metadata with image analysis. This will require methodological standardization and validation in real-world settings.
2026 pásztor2026the DATABASE
[The application of artificial intelligence in healthcare: benefits, challenges, and issues of responsibility].

Pásztor, Ibolya; Papp, Annamária; Bíró, Klára; Bányai-Márton, Gábor; Papp, Zoltán

Orvosi hetilap , 167 : 1079-1087

Artificial intelligence is playing an increasingly important role in healthcare, particularly in diagnostics, clinical decision support, personalized medicine, robotics, administration, and medical education. At the same time, the clinical use of artificial intelligence raises not only technological opportunities but also ethical, data protection, and legal challenges. The aim of our study was to provide an integrated overview of the main benefits and risks of artificial intelligence applications in healthcare, with particular emphasis on liability-related issues. We conducted an interdisciplinary, narrative critical review of the literature. Scientific and regulatory sources were analyzed using content analysis, thematic analysis, and critical discourse analysis, with the integration of clinical, ethical, legal, and data protection perspectives. Based on the reviewed literature, artificial intelligence may improve diagnostic accuracy, support personalized decision-making, reduce administrative burden, contribute to better access to care, and create new opportunities in robotics and medical education. However, algorithmic bias, the lack of transparency resulting from the "black box" nature of many artificial intelligence systems, data protection and cybersecurity risks, as well as uncertainties related to social acceptance, represent major challenges. Our most important finding is that these issues converge in the question of clinical liability, while the traditional physician-centered model of responsibility appears increasingly insufficient. The use of artificial intelligence in healthcare can only be sustainable and legitimate within an integrated, liability-centered framework. Reconsidering the distribution of responsibilities among developers, healthcare institutions, and clinicians, as well as strengthening institutional guarantees of transparency, human oversight, patient autonomy, and data protection is essential for the safe implementation of artificial intelligence in healthcare. Orv Hetil. 2026; 167(27): 1079-1087.
2026 soleimani sardou2026artificial DATABASE
Artificial intelligence for oral cancer diagnosis: a systematic review and meta-analysis of image-based and non-imaging models.

Soleimani Sardou, Shima; Rezvaninejad, Raziyehsadat; Rezvaninejad, Fatemeh Sadat; Nekouei, Amir Hossein

BMC cancer

Artificial intelligence (AI) is increasingly recognized as a valuable tool for the early detection and prognosis of oral cancer, addressing the challenge of high mortality due to late diagnosis. Artificial intelligence based diagnostic models have the potential to improve accuracy in differentiating between malignant, premalignant and benign oral lesions. This systematic review and meta-analysis evaluated the diagnostic performance of non-imaging and image-based artificial intelligence models and narratively synthesized evidence on prognostic and risk stratification applications in oral cancer. This study follows PRISMA guidelines to ensure quality and reproducibility. A systematic search across PubMed, Embase, Web of Science, Google Scholar and Scopus identified studies from 2010 to 2024 on artificial intelligence applications in oral cancer diagnosis. Sixteen eligible studies met predefined inclusion criteria, including AI-based screening compared to histology. Data extraction and bias assessment were conducted independently using QUADAS-2. The findings highlight AI's potential in early detection and prognosis, emphasizing the need for further validation and clinical integration to enhance diagnostic accuracy. A total of 801 studies were initially identified, with 53 undergoing further review, ultimately selecting 16 studies. Sample sizes varied from 70 to 44,000, allowing a broad evaluation of AI's diagnostic performance. Artificial intelligence models showed wide range of sensitivity (42%-100%), specificity (63%-100%), and accuracy (63%-100%). Meta-analysis revealed a pooled sensitivity of 0.90 (95% CI: 0.81-0.98), specificity of 0.89 (95% CI: 0.84-0.95), and accuracy of 0.89 (95% CI: 0.83-0.95), with substantial heterogeneity (I² = 100%). Image-based models had higher pooled sensitivity (0.94 vs. 0.76, P = 0.320), specificity (0.93 vs. 0.79, P = 0.025), and accuracy (0.93 vs. 0.81, P = 0.042). Artificial intelligence models show promising diagnostic performance for oral cancer based on retrospective clinical data. Although image-based models, particularly convolutional neural networks, demonstrated higher pooled sensitivity and specificity than non-imaging models, these differences were not statistically significant. Results should be interpreted with caution due to substantial heterogeneity. Advances reported in the literature, such as multimodal approaches and data augmentation, may improve non-imaging model performance and help narrow the gap between methodologies. These developments highlight AI's potential in enhancing early detection and prognosis of oral cancer.
2024 zhao2024how DATABASE
How does artificial intelligence promote renewable energy development? The role of climate finance

Congyu Zhao; Kangyin Dong; Kung-Jeng Wang; Rabindra Nepal

Energy Economics

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.
2023 liu2023games DATABASE
Games for Artificial Intelligence Research: A Review and Perspectives

Chengpeng Hu; Yunlong Zhao; Ziqi Wang; Haocheng Du; Jialin Liu

arXiv Preprint

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.
2021 filho2021watershed DATABASE
Watershed of Artificial Intelligence: Human Intelligence, Machine Intelligence, and Biological Intelligence

Li Weigang; Liriam Enamoto; Denise Leyi Li; Geraldo Pereira Rocha Filho

arXiv Preprint

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.