Knowledge, attitudes, and practices toward artificial intelligence in medicine among Chinese physicians: A cross-sectional study from January to March 2024 with analysis of influencing factors.

As the use of artificial intelligence (AI) gradually becomes an integral part of contemporary work settings, the awareness of AI, which can be defined as the extent to which employees feel that their jobs can be replaced by the automated systems, has proven to be a potentially influential factor of affective and psychosocial consequences, such as affective states, work-family balance, and emotional well-being on the whole. The present investigation aims to clarify the mediating variables that relate AI awareness to emotional exhaustion, in particular, the mediating role of perceived job insecurity, work demands, and family obligations. The study used a convenience sample of 303 employees (49.8% men) and conducted hierarchical regression models with bootstrap resampling to investigate mediation. The analytic approach included direct effect, indirect paths of AI awareness to emotional exhaustion and their serial mediation, and then the indirect path of the same to work insecurity and through work-family interference. The results showed that there was a significant positive relationship between AI awareness and emotional exhaustion. Moreover, AI awareness was positively related to perceived job insecurity, which, in turn, was positively related to emotional exhaustion. Parallel analyses indicated that AI awareness was also associated with increased work-family interference, which in turn was associated with increased emotional exhaustion. More importantly, job insecurity and work-family interference sequentially mediated the relationship between AI awareness and emotional exhaustion, suggesting a compounded negative pathway. Such findings highlight the urgent need for organizational leaders to address the twin issues of maintaining job security and achieving a balanced work-life environment, given that AI is being introduced as a source of workplace stress. In practice, it will entail open discussion of AI implementation, the design of overall reskilling programs, and the introduction of flexible working models to help reduce AI-related stressors and protect employees' psychological well-being.

Healthcare systems worldwide face unprecedented challenges, including escalating costs, workforce shortages, and access disparities, which threaten their sustainability. The WHO projects an 18 million healthcare worker deficit by 2030, while financial and geographical barriers prevent millions from receiving necessary care. The integration of Artificial Intelligence (AI) into healthcare delivery systems presents opportunities to transform medical service provision, accessibility, and experiences, potentially democratizing healthcare access. This perspective analysis employs a theoretical framework combining Levesque etal.'s patient-centered healthcare access model with AI democratization frameworks. The analysis synthesizes current evidence on AI healthcare applications and proposes an implementation framework encompassing four dimensions: accessibility, affordability, usability, and ethical regulation. The framework addresses stakeholder roles and governance mechanisms aligned with international standards including the EU's AI Act and WHO's AI ethics guidance. Evidence demonstrates significant democratization potential through the implementation of AI. AI-powered platforms eliminate geographical barriers, reduce diagnostic timeframes, optimize resources, and enhance preventive care. Implementation challenges include algorithmic bias, data privacy concerns, digital divide risks, and regulatory fragmentation. AI integration holds transformative potential for democratizing healthcare across demographic and socioeconomic boundaries. Successful implementation requires structured, ethically grounded approaches that prioritize accessibility, affordability, usability, and regulation while maintaining a human-centered care approach. The framework offers actionable guidance for healthcare professionals and policymakers on deploying AI technologies to reduce disparities. Continuous research, interdisciplinary collaboration, and robust governance are crucial to ensuring that AI advances healthcare equity while preserving patient autonomy and clinical judgment. Patient and Public Involvement and Engagement was not appropriate for this theoretical framework and perspective analysis, as it represents a conceptual synthesis of existing literature and policy frameworks rather than primary research involving human participants. This manuscript establishes a theoretical foundation and an implementation framework for AI-driven healthcare democratization, grounded in published evidence and established models of healthcare access. The work focuses on guiding healthcare policymakers and planning professionals rather than collecting new data from patients or the public. However, the framework explicitly emphasizes the critical importance of patient advocacy organizations and community representation in AI development processes, recognizing that meaningful patient involvement will be essential during the actual implementation phases of AI healthcare technologies described in this theoretical foundation.

Role of artificial intelligence augmented interventions in Rehabilitation: A scoping review.

Evolution of artificial intelligence at Hospital Italiano de Buenos Aires: A retrospective review of experience and lessons learned.

The role of artificial intelligence in virtual emergency care: a systematic review.

Governance Framework for Safe and Ethical Implementation of Artificial Intelligence in Surgery: A Modified Delphi Consensus.

Implementation of artificial intelligence to automate physical disector in a fractionator design for quantification of stem cell-derived neurons.

With the rapid implementation of artificial intelligence (AI) in radiographer workflows, leadership roles are necessary for its safe and effective integration into practice. Due to their dual professional identity (encompassing patient-centred care skills and technical skills) radiographers emerge as natural AI leaders within the medical imaging and radiotherapy ecosystems. To examine how UK radiographers perceive their readiness, confidence and potential roles in AI leadership, and to identify the barriers and enablers for their engagement within the AI-ecosystem. A UK-wide, cross-sectional, online survey of radiographers and students (n=273) combined demographic questions, AI knowledge and experience questions, Likert-type assessments of preparedness and free text responses. Quantitative data were analysed using descriptive statistics and Mann-Whitney U tests; qualitative data underwent thematic content analysis. Most respondents reported limited AI literacy and minimal hands-on experience, citing insufficient education, protected time and managerial support as key barriers to leadership readiness. Confidence varied: women and those with little AI exposure, expressed statistically significant lower confidence to lead in AI-enabled environments. Respondents felt more comfortable taking on leadership responsibilities once AI systems were already in place than leading their implementation. Qualitative findings indicated that in this predominantly frontline sample, radiographers described AI leadership mainly as operational, practice-based work. Motivations for leadership focused on improving workflows, supporting colleagues and ensuring safe practice. Radiographers recognise the relevance of AI leadership but understand it as practice-proximal, operational-focused responsibilities, due to limited AI exposure, uneven confidence and the absence of defined leadership pathways in national policy. Role ambiguity and limited experiential learning constrain radiographers' ability to envision strategic or organisation-wide AI leadership. Profession-specific education, structured experiential opportunities and organisational support are essential for enabling radiographers to participate equitably and effectively in AI-enabled service transformation.

Artificial intelligence (AI) is poised to transform diagnostic radiology, yet data on its adoption and the perspectives of radiologists in the Middle East remain scarce. This study provides the first comprehensive analysis of AI engagement among radiologists in the United Arab Emirates (UAE), a nation characterized by substantial investment in digital health infrastructure and artificial intelligence initiatives. We conducted a cross-sectional survey of 100 practicing radiologists in the UAE. The survey assessed professional role, practice setting, institution characteristics, attitudes toward AI, adoption patterns, perceived clinical impact, and preferences for future AI applications. The study revealed a remarkably high rate of AI adoption. Sixty-seven percent of respondents reported daily AI use. Attitudes were overwhelmingly positive, with 73% of radiologists holding a favorable or very favorable view of AI. We identified a significant gap between desired and currently available AI applications, particularly in abdominal imaging (24% gap), pediatric imaging (18% gap), and emergency/trauma imaging (16% gap) (p = 0.031). Despite high adoption, there was no statistically significant difference in attitudes or daily use across professional roles or practice settings (p > 0.05). A strong preference for a cautious, evidence-based approach was evident, with 71% of respondents favoring gradual AI implementation. Radiologists in the UAE reported high levels of AI adoption and generally favorable attitudes toward AI-assisted radiology practice, with high expectations for its future development. The findings highlight a critical need for targeted investment in underdeveloped AI subspecialty tools to meet clinical demand. These results provide a valuable benchmark for the region and underscore the importance of aligning AI development with the practical needs of clinical radiologists to ensure successful and impactful integration.

The growing integration of artificial intelligence (AI) into corporate managerial functions has transformed the methods used to monitor and evaluate employee performance. However, it has also increased concerns regarding privacy infringements and algorithmic bias, which may result in disciplinary measures or even employment termination. This study aims to examine the limits of employers’ authority in the use of AI-based performance monitoring systems and to analyze the legal protections available to employees subjected to automated performance evaluations. The research employs a normative legal methodology using statutory and conceptual approaches. The findings reveal that the implementation of AI in employment management must adhere to the principles of legality, transparency, purpose limitation, and human oversight. Furthermore, employee protection can be strengthened through effective complaint mechanisms, procedural fairness, and algorithmic accountability. The study concludes that, although an initial legal framework exists, further regulatory development is necessary to ensure comprehensive protection of employees’ privacy rights and human dignity in the context of AI-driven workplace management.

Purpose This paper aims to examine how artificial intelligence (AI) is reshaping academic experiences for students with physical disabilities (SWPDs) in Kenyan universities. It explores the practical role of AI in enhancing accessibility, independent learning, communication, classroom participation, mobility, assessment and digital inclusion within university academic environments. Design/methodology/approach The paper adopts a practical and experience-based approach informed by observations from teaching spaces, e-learning environments, computer laboratories, disability support offices, student service centers and ICT departments across selected Kenyan universities. Insights are drawn from institutional practices, emerging AI applications, accessibility experiences and current literature on inclusive higher education and AI-supported learning. Findings AI technologies are increasingly improving academic participation for SWPDs through speech-to-text applications, AI-powered note-taking tools, automated captioning systems, smart mobility applications, adaptive learning platforms, AI chatbots, predictive writing tools and intelligent assistive technologies. These tools support independent learning, reduce physical strain, improve communication, and enhance participation in face-to-face, blended and online learning environments. However, implementation remains uneven due to inadequate infrastructure, unreliable internet connectivity, limited staff competencies, affordability challenges, weak institutional policies, and insufficient involvement of students with disabilities in AI planning and deployment. In many universities, AI adoption focuses on general digital transformation without fully integrating accessibility and disability inclusion considerations. Research limitations/implications This study is limited by its practical and experience-based approach, which relied mainly on observations, institutional experiences and existing literature rather than large-scale empirical data collection across all Kenyan universities. The selected universities may not fully represent the varying technological capacities and accessibility practices of all higher education institutions in Kenya. In addition, the rapidly evolving nature of AI technologies may affect the long-term relevance of some findings and recommendations. The study also focused specifically on students with physical disabilities and did not extensively explore the experiences of students with other forms of disabilities, while limited institutional documentation and resource constraints further restricted deeper analysis of AI accessibility implementation practices. Practical implications Kenyan universities should adopt inclusive AI implementation frameworks that integrate accessibility into teaching, assessment, student support, digital learning and campus services. Institutions should prioritize affordable AI solutions, continuous staff training, participatory technology planning, and accessible digital infrastructure to improve academic inclusion and student independence. Social implications The integration of AI in Kenyan universities has significant social implications for students with physical disabilities by promoting educational inclusion, independent learning, social participation and digital equity. AI-powered tools such as speech recognition systems, adaptive learning platforms, automated captioning and smart accessibility applications help reduce barriers to academic participation, improve confidence and autonomy, and enhance interaction within university environments. These technologies also contribute to preparing students for participation in technology-driven workplaces and broader socioeconomic activities. However, unequal access to AI infrastructure, affordability challenges, limited institutional capacity and inadequate accessibility planning may widen existing digital and educational inequalities if not addressed effectively. Consequently, universities must adopt inclusive and participatory AI implementation approaches that ensure equitable access, ethical use of technology, and sustainable support for students with physical disabilities. Originality/value This paper contributes practical insights into how AI can support inclusive higher education for students with physical disabilities in Sub-Saharan Africa. It highlights context-specific experiences from Kenyan universities and demonstrates how AI can move institutions beyond structural accessibility toward functional and participatory inclusion in academic environments.

ObjectiveIn emergency trauma care, artificial intelligence (AI) may aid fracture detection on radiographs, potentially reducing radiologists’ workload. We evaluated the role of deep learning-based decision-support software in the reporting of trauma cases.Materials and methodsWe retrospectively analyzed 2317 trauma radiographs acquired at a single center: 1,174 images obtained from November 1 to 16, 2023, without access to the AI tool during reporting, and 1,143 images from February 1 to 13, 2024, with discretionary use of the AI output during reporting. The AI software output was compared with final radiology reports, with ground truth established by a musculoskeletal radiologist with 9 years’ experience. Accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated at both the fracture and patient levels.ResultsThe dataset included 1,914 patients with 1,188 acute fractures (621 in November, 567 in February). At the fracture level, standalone AI achieved 90.7% accuracy, 87.8% sensitivity, 94.0% specificity, 94.3% PPV, and 87.2% NPV in November, 94.1%, 93.5%, 94.6%, 94.5%, and 93.6% in February, respectively. Non-AI-assisted radiologists reached 92.4%, 89.0%, 96.2%, 96.3%, and 88.7%, AI-assisted radiologists 93.4%, 90.0%, 96.7%, 96.4%, and 90.7%, respectively. At the patient level, AI’s overall performance reached up to 96.5% accuracy and 95.6% sensitivity. Discrepancies between AI and radiologists occurred in 326 cases, often related to anatomical variants such as accessory ossicles.ConclusionStandalone AI demonstrated near-expert accuracy and sensitivity in fracture detection at both fracture and patient levels. PPV increased with AI support, indicating more accurate detection of actual fractures.Relevance statementBy examining discretionary real-world use of AI in trauma radiography, this study shows that clinical benefit is not guaranteed by algorithmic performance alone, as optional AI integration does not consistently improve radiologist sensitivity, underscoring a critical implementation gap in practice.Key PointsStandalone AI achieves near-expert fracture detection performance in trauma radiography.Discretionary AI use does not consistently improve radiologist sensitivity.AI use reduces discrepancies, suggesting improved diagnostic consistency.Clinical benefit of AI depends on real-world implementation strategy.Graphical

The rapid advancement of artificial intelligence (AI) is fundamentally transforming human resource management (HRM), creating new opportunities for organizations to enhance workforce effectiveness, improve strategic decision-making, and strengthen long-term competitiveness. Despite growing scholarly attention to AI-enabled HRM practices, existing research remains fragmented and lacks a comprehensive theoretical framework explaining how AI contributes to sustainable organizational performance while balancing the interests of diverse stakeholders. This article addresses this gap by integrating Dynamic Capabilities Theory and Stakeholder Theory to develop a novel conceptual framework for AI-enabled human resource management. Drawing upon Dynamic Capabilities Theory, the study explains how AI technologies enhance organizational capabilities through sensing workforce needs, seizing strategic opportunities, and reconfiguring HR processes to respond to changing business environments. Simultaneously, Stakeholder Theory provides a complementary perspective by emphasizing the importance of creating value for employees, managers, customers, investors, and broader society through responsible and ethical AI implementation. The proposed framework demonstrates how AI-enabled HRM practices, including talent acquisition, employee development, performance management, workforce analytics, and employee engagement, contribute to organizational agility, workforce resilience, stakeholder satisfaction, and sustainable organizational performance. The article further develops a series of research propositions and identifies future research directions for scholars examining the intersection of artificial intelligence, human resource management, organizational sustainability, and strategic management. The framework offers both theoretical and practical insights for organizations seeking to leverage AI technologies while maintaining stakeholder trust and achieving long-term organizational success.

Abstract The adoption of Artificial Intelligence (AI) in the healthcare sector offers an unprecedented opportunity to revolutionize global health systems by addressing growing challenges related to efficiency, accessibility, and quality of care. This systematic review explores the opportunities, challenges, enablers, and barriers associated with AI adoption in healthcare, integrating professional, organizational, and patient perspectives. Through an in-depth analysis of the literature, this work highlights an integrated set of determinants for AI adoption, including technological, economic, regulatory, and cultural aspects, with particular emphasis on barriers such as perceived threats to professional autonomy, privacy concerns, and infrastructural gaps. As a result, the study develops an evidence-informed integrative framework that explores the interactions between external determinants, such as macroeconomic, technological, and regulatory readiness, and internal factors, including organizational and user readiness. Furthermore, it highlights recurrent knowledge-alignment and coordination challenges between clinical and IT specialists (hereafter referred to as Knowledge-proximity), emphasizing the need for greater integration and mutual understanding to support effective AI implementation. This integrative framework synthesizes multidisciplinary perspectives and provides actionable implications for policymakers, AI providers, and healthcare institutions, with relevance across diverse healthcare contexts and stages of implementation. This research bridges the gap between technological development and real-world implementation, providing a foundation for future studies and evidence-informed strategies to support AI adoption in health services.

This article analyzes the didactic potential and methodological challenges of integrating artificial intelligence (AI) technologies within the digital ecosystem of primary education. The study highlights the capabilities of AI in supporting personalized learning, differentiated instruction, formative assessment, reading literacy, mathematical competence, language development, and creative thinking. Furthermore, the paper provides a scientific evaluation of risks such as algorithmic bias, data privacy concerns, the lack of teacher readiness, the digital divide, over-reliance on automated answers, and the specific cognitive and emotional needs of young learners. Consequently, the article proposes a methodological model, safety principles, and practical recommendations for the effective implementation of AI in the classroom

IntroductionEmergency Departments (EDs) in Africa face significant challenges including resource scarcity, overcrowding, and limited infrastructure. Artificial intelligence (AI) presents a promising opportunity to enhance emergency care delivery in these settings. Despite growing global interest, little is known about the perceptions, experiences, and readiness of African emergency medicine professionals regarding AI integration. This study evaluated the knowledge, perceived advantages, concerns and support requirements related to AI among emergency medicine professionals across sub-Saharan Africa.MethodsA cross-sectional mixed-method study was conducted among emergency medicine consultants and residents across 14 African countries. Data was collected via a self-administered online questionnaire adapted from a previously validated instrument and distributed through professional networks. Quantitative items captured demographic information, AI knowledge, usage, and perceptions, while open-ended qualitative questions explored experiences, expectations, and barriers. Descriptive statistics summarized quantitative data, and inductive thematic analysis was applied to qualitative responses. Cross tab and fisher exact analysis was done to assess association.ResultsA total of 211 responses were analyzed (median age 32 years; 72.5 % male; 65.9 % consultants). Most respondents had a basic understanding of AI (88.2 %) and were aware of AI applications in emergency medicine (73.2 %), yet only 14.2 % had received formal training. While 73.0 % had used AI tools, with predominantly nonclinical use (research 31.8 % and medical writing 20.1 %) only 29.9 % reported routine clinical use. Only12.0 % indicated that their institution had a formal AI implementation strategy. Respondents expressed concerns regarding AI errors (99.1 %), ethical risks (93.8 %), job displacement (88.6 %), and high cost (85.3 %). The majority (64.5 %) identified training as the most critical support needed, followed by policy guidance (21.3 %). Overall, 78.0 % expected AI to be used in African EDs in the future, although many emphasized the importance of gradual, contextually appropriate integration with sustained human oversight.ConclusionAfrican emergency medicine professionals are aware of AI and recognize its potential benefits, but formal training, institutional strategies, and infrastructure remain limited. Optimizing AI adoption requires structured education, policy development, context-specific implementation strategies, and ethical safeguards. These findings provide actionable insights for the safe and effective integration of AI in resource-limited emergency care settings across Africa.

Artificial intelligence in dermatology: Clinical promise and environmental impact.

Debriefing is widely recognised as a central mechanism for learning within healthcare simulation, enabling learners to reflect on clinical actions, decision-making, and team interactions. However, high-quality debriefing is resource-intensive, dependent on facilitator expertise, and increasingly challenged by the growing complexity and volume of data generated during modern simulation activities. Artificial intelligence (AI) offers emerging opportunities to augment aspects of debriefing by analysing performance data, structuring reflective dialogue, and supporting learning environments. This article explores the emerging role of AI within debriefing. Drawing on the current literature, we describe four modes of AI being integrated into debriefing practice: metric-based AI tutors, large language model-assisted debriefing tools, conversational chatbot debriefers and hybrid integrated AI systems. For each mode, we examine their underlying mechanisms, current applications, and current contributions to, and limitations within, debriefing. Using these four modes as a scaffold, we offer practical guidance for simulation practitioners considering the integration of AI tools within their own practice, including considerations related to faculty AI literacy, educational alignment, governance, and implementation. While the empirical evidence base is evolving, AI-driven approaches offer new ways of supporting facilitators in augmenting reflective practice. When implemented thoughtfully and with appropriate human oversight, the integration of AI into debriefing portends a new era supporting reflective learning within healthcare simulation.

Artificial intelligence (AI) has demonstrated strong potential in breast cancer diagnostics by improving accuracy, efficiency, and clinical workflow. However, adoption among physicians remains variable. Existing research often overlooks the contextual and experiential differences between clinicians who use AI and those who do not. A comprehensive understanding of barriers and facilitators, especially across user groups, is essential to inform equitable and effective AI implementation in real-world settings. This study aimed to (1) identify key barriers and facilitators influencing the use of AI tools in breast cancer diagnostics, with a specific focus on comparing current users and nonusers, and (2) examine how social, technological, and individual-level factors are linked to physicians' attitudes toward AI, intention to use it, and perceived likelihood of future adoption. A cross-sectional, embedded mixed methods survey was conducted with 46 Austrian physicians. Quantitative items were based on the technology acceptance model and its extensions. Open-ended responses were analyzed using conventional content analysis and integrated with quantitative results via joint displays. Ordinary least squares regressions examined factors associated with attitudes, intention, and the likelihood of future AI use. Among the 46 participating physicians, 52% (n=24) reported current AI use. Common facilitators included improved quality of work, efficiency, and expanding knowledge. Nonusers highlighted barriers such as limited access (17/21, 81%), high costs, and lack of training. AI users highlighted barriers related to limited integration with existing systems and concerns about trust. Despite these differences, both groups expressed strong future adoption intentions. Perceiving multiple facilitators was significantly associated with more favorable attitudes (B=0.83; P=.02), stronger intention to use AI (B=1.32; P=.01), and higher perceived likelihood of future use (B=1.56; P=.001). AI-related skills positively predicted intention (B=1.00; P=.04) and likelihood of future use (B=1.16; P=.01), while colleagues' positive views about AI predicted both attitudes (B=0.34; P=.02) and intention (B=0.39; P=.01). In contrast, perceiving multiple barriers was associated with lower intention (B=-0.84; P=.047) and likelihood (B=-1.48; P<.001). Being aged 50 or older was significantly associated with more negative attitudes (B=-1.11; P=.002) and lower likelihood of future use (B=-0.82; P=.02). This study offers preliminary insights into the implementation of AI in breast cancer diagnostics within the Austrian health care context. AI adoption appears to be a staged process with evolving support needs. Early-stage users may benefit from improved access and training, while experienced users require support for workflow integration and trust-building. Promoting peer support, addressing demographic disparities, and embedding AI training into clinical routines may support more sustainable and equitable adoption. These findings inform tailored implementation strategies and offer recommendations that may be transferable to other health systems.