With the increasing embeddedness of AI robots and other intelligent technologies in organizational workplaces, leader AI-focused attention has emerged as an important reference point for employees as they use and adapt to AI-related technologies. Drawing on work-related rumination theory, this study develops and tests an integrated mediation model to examine how leader AI-focused attention is related to employee proactive behavior through two parallel pathways: problem-solving pondering and affective rumination. It further investigates the moderating role of AI job role clarity. Based on structural equation modeling of multi-wave survey data from 514 employees, the results show that leader AI-focused attention positively predicts employees' problem-solving pondering and affective rumination. Problem-solving pondering is positively related to employee proactive behavior, whereas affective rumination is negatively related to employee proactive behavior. In addition, AI job role clarity positively moderates the relationship between leader AI-focused attention and problem-solving pondering; specifically, this positive relationship is stronger when employees report higher AI job role clarity. From the perspective of work-related rumination, this study extends the explanation of the psychological mechanisms linking leader AI-focused attention to employee proactive behavior. It also provides theoretical insights and practical implications for understanding the boundary condition of leaders' attentional signals in AI-related work contexts and for supporting employee proactive behavior.
Recent advances in graphics processing units (GPUs) have substantially broadened the applicability of computational chemistry. Nevertheless, the double-precision (FP64) operations conventionally required in quantum chemistry remain inefficient on artificial intelligence (AI)-focused GPUs, which are predominantly optimized for lower precision arithmetic. Moreover, the limited memory capacity of such GPUs necessitates algorithmic adaptations to traditional approaches in density functional theory (DFT). In this work, we propose a systematic mixed precision strategy for the iterative matrix diagonalization procedure, which constitutes the principal bottleneck in real-space DFT calculations. By selectively employing single-precision (FP32) in most computational steps and even adopting brain floating point (BF16) in the preconditioning stage, we demonstrate that numerical accuracy can be preserved with negligible degradation. Building on these observations, we developed a mixed precision eigensolver and validated its performance across material systems spanning a wide range of sizes. Our results show that the proposed method achieves up to 10× speedup in the diagonalization step relative to FP64, while extending the feasible system size by approximately 50%. Comprehensive validation across multiple GPU architectures further confirms that AI-focused GPUs can deliver performance comparable to that of high-end high-performance computing (HPC)-focused GPUs, thereby substantially improving accessibility to large-scale electronic structure simulations.
Artificial intelligence (AI) is increasingly applied in clinical diagnostics, particularly in radiology, where it can assist with imaging triaging and anomaly detection. However, the integration of AI into medical education remains under researched. This study investigates the impact of an AI-focused panel discussion on medical students' perceptions, knowledge, attitudes and concerns about AI in radiology. A paired pre-post design questionnaire comprising of 13 five-point Likert scale questions was administered to 40 medical students to complete before and after an AI-focused educational panel session at the International Radiology Undergraduate Symposium in London, United Kingdom on 24th November 2024. The questionnaire assessed four domains: 'Understanding of AI,' 'Attitudes Toward AI in Radiology,' 'AI Education in Medical School,' and 'Concerns About AI in the Future.' The primary outcome was to assess the change in students' perceptions of AI's role in radiology. Differences between pre- and post-session responses were analysed using the Wilcoxon signed-rank test. The Hodges-Lehmann median difference, the effect size, r, and their corresponding 95% confidence intervals were calculated, and p-values were adjusted using the Holm-Bonferroni method. Of the 81 eligible attendees, 40 (49.4%) completed the questionnaire (39 pre-session, 40 post-session). Students demonstrated significant improvements in their understanding of AI's potential role in radiology (Z = 3.04, p = 0.002; Holm-Bonferroni = 0.029; median paired difference = 0.5, 95% CI 0.0-0.5; r = 0.49, 95% CI 0.25-0.68) and in their awareness of AI's broader clinical applications (Z = 3.65, p < 0.001; Holm-Bonferroni = 0.0035; median paired difference = 0.5, 95% CI 0.5-1.0; r = 0.60, 95% CI 0.38-0.75). Participants expressed a more positive view of AI in healthcare overall, although concerns about AI replacing radiologists and insufficient AI education persisted. Educational interventions have the potential to improve medical students' understanding and attitudes toward AI in radiology. Integrating structured AI education into undergraduate curricula may enhance AI literacy and better prepare future clinicians for an AI-enabled healthcare environment.
This study investigates how organizational communication influences employee creativity in China's high-tech industries. Drawing on the diffusion of innovations theory, we examine the effects of tech-driven supervisory communication on creative output in high-tech environments, exploring the mediating roles of employee vigor at work and peer AI-support and the moderating role of AI-enabled creative Utilization. Data were collected from 483 employees and their supervisors in Beijing, Shanghai, Guangzhou, and Chengdu in three waves. Findings from SmartPLS analyses reveal that clear, technologically focused supervisory communication significantly boosts creative output both directly and indirectly through enhanced employee vigor and supportive peer interactions. Moreover, employees' active utilization of AI strengthens the positive influence of supervisory communication on vigor, underscoring the importance of digital competencies in today's competitive landscape. These results contribute to a more integrated understanding of the organizational and personal factors that foster creativity and offer practical insights for high-tech organizations seeking to build innovative cultures in rapidly evolving market environments.
Michael Kratsios and Lynne Parker, veterans of Trump's first term, come with technology backgrounds.
The integration of Generative artificial intelligence (GAI) into healthcare is rapidly evolving, necessitating ethical preparedness among nursing students. GAI technologies present ethical challenges related to patient privacy, algorithmic bias, and informed consent, underscoring the need for structured AI ethics education in nursing curricula. This study aims to examine the impact of an ethics education program on nursing students' AI ethical awareness, moral sensitivity, attitudes, and intentions to use GAI in healthcare. A quasi experimental, pretest‒posttest study was conducted with 115 nursing students. The participants were randomly assigned to an intervention group (n = 57), which received a structured AI ethics education program, or a control group (n = 58), which did not receive the intervention. The validated scales measured AI ethical awareness, moral sensitivity, attitudes, and the intention to use AI before and after the intervention. Compared with the control group, the intervention group demonstrated a significant increase in AI ethical awareness (M = 57.28, SD = 22.28) (M = 47.43, SD = 24.04; p = .025, η² = 0.044). Moral sensitivity also showed a notable improvement in the intervention group (M = 74.33, SD = 29.93) compared with the control group (M = 60.26, SD = 22.52; p = .005, η² = 0.067). Compared with the control group, positive attitudes toward AI significantly increased postintervention (M = 39.46, SD = 11.51) (M = 23.21, SD = 11.72; p < .001, η² = 0.332), indicating a strong effect of ethics education. Furthermore, the intention to use AI technology improved significantly in the intervention group (M = 12.46, SD = 3.55) compared with the control group (M = 10.24, SD = 3.15; p = .001, η² = 0.099). However, negative attitudes toward GAI did not significantly change postintervention. This study highlights the effectiveness of structured AI ethics education in enhancing ethical competencies among nursing students. Integrating such programs into nursing curricula is essential to prepare future nurses for ethical decision-making in AI-driven healthcare. These findings support the development of standardized ethics training modules to guide responsible AI use in clinical practice and inform future curriculum design. Not applicable.
AI education is essential to facilitate seamless clinical integration. The HCPC in the UK requires all radiographers to have some level of digital skills to maintain safety of clinical practice. This study aimed to evaluate the impact of a dedicated AI seminar on radiography students. A dedicated 1.5-h in-person seminar was delivered by an AI vendor to final year undergraduate diagnostic radiography students at a UK University. The course consisted of both theory and practice training. An online survey was built and piloted, consisting of both closed and open-ended questions, to explore their level of knowledge, skills and confidence in AI, before (pre-test) and after the delivery (post-test) of the seminar using a 10-point scale. Pre-test was distributed two weeks before the seminar and post-test was open two weeks after. A total of 68 students answered the pre-test and 31 the post-test survey. Students' theoretical knowledge (Mean = 6.57 vs Mean = 3.85), skills (Mean = 5.39 vs Mean = 3.44) and confidence (Mean = 5.47 vs Mean = 3.43) on AI were all significantly improved after the seminar. Their responses became more focused and specific in the post-test survey. In both surveys students expressed concerns around reliability and accountability of AI, data management and security, patient confidentiality and overreliance on technology in the open-ended questions. They also requested more AI training with hands-on options in their undergraduate degree. This study confirms the importance of even brief, but customised educational interventions relating to AI for radiographers. The learning needs to be customised to maximise knowledge retention and applicability and to include both theoretical and practical aspects for consolidation of skills. These findings will help radiography educators build more focused, tailored AI courses for future students.
Artificial Intelligence is rapidly transforming the education of healthcare professionals. Despite this progress, many healthcare educators lack the necessary knowledge and confidence to integrate AI effectively. Structured faculty development initiatives may address this gap by enhancing educators' capacity to incorporate AI. This study investigated participants' perceptions of a series of AI-focused capacity-building workshops conducted in Pakistan and explored the sustained effect of these workshops on educators' attitudes, confidence, and application of AI tools in educational settings. A Prospective observational follow-up study was conducted across five workshops: AI in Research (n = 18), AI in Simulation (n = 6), AI in Gamification (n = 15), AI in Assessment (n = 23), and AI in Prompt Engineering (n = 27). Immediate post-workshop surveys measured perceived significance, satisfaction, and knowledge gains. A follow-up survey three months later evaluated sustained use, behavioral change, and institutional dissemination. The follow-up survey questionnaire included the application of workshop learning, changes in attitude, skills, and confidence, institutional support, reflection, and future directions. Quantitative data were analyzed using descriptive statistics, while qualitative responses were subjected to thematic analysis. Participants reported high satisfaction across all workshops, with over 85% rating the sessions as "Excellent" or "Satisfactory" in terms of achieving learning objectives, knowledge gain, and applicability. Follow-up data (n = 56) demonstrated sustained impact: 85% of participants reported using at least one AI tool in teaching or research, 90% expressed increased openness to AI use, and 77% shared their learning with colleagues. Commonly cited challenges included inadequate infrastructure, institutional resistance, and ethical concerns. AI-focused faculty development workshops significantly enhanced educators' knowledge, skills, confidence, and motivation to incorporate AI into health professions education. The study uniquely contributes follow-up evidence on early capacity building and educators' application of AI tools after AI-focused faculty development workshops in health professions education.
Ramezanzade S, Dascalu TL, Bakhshandeh A, Uribe SE, Ibragimov B, Bjørndal L. The impact of training dental students to use an artificial intelligence-based platform for pulp exposure prediction prior to deep caries excavation: a proof-of-concept randomized controlled trial. Int Endod J. 2026;59:1248-56. https://doi.org/10.1111/iej.70046 DESIGN: This randomized controlled trial (RCT) evaluated whether a structured educational intervention could improve dental students' interaction with an artificial intelligence (AI)-based decision-support system developed to predict pulp exposure before excavation of deep carious lesions. Eighteen dental students were randomly allocated to either an experimental group receiving a one-hour personalized training session on the use of the AI platform or a control group receiving a brief introductory video. Participants subsequently completed a case-based assessment involving radiographic evaluation of deep carious lesions and prediction of pulp exposure risk using the AI system. The primary outcome was agreement with AI recommendations ("agreeableness with AI"). Secondary outcomes included diagnostic accuracy, sensitivity, specificity, F1-score, and response time. Outcomes were compared between groups, and the findings were used to estimate the sample size required for a future definitive trial. Participants who received AI-focused training demonstrated greater agreement with AI recommendations than controls. However, improvements in agreement were not accompanied by meaningful differences in diagnostic accuracy, sensitivity, specificity, or F1-score. Response times were slightly shorter among trained participants. The findings suggest that targeted instruction may influence how users interact with AI systems, although objective diagnostic performance remained largely unchanged. A short, personalized training session may increase dental students' agreement with AI-generated predictions of pulp exposure during deep caries excavation. However, the intervention did not substantially improve diagnostic performance, and no patient-centered outcomes were assessed. Larger studies are needed to determine whether AI training can enhance clinical decision-making and improve outcomes relevant to the management of deep carious lesions.
Artificial intelligence (AI) is rapidly reshaping higher education, offering both opportunities and challenges for teaching and learning. As generative AI tools become increasingly accessible, instructors must balance the benefits of enhanced productivity with the responsibility to teach students how to engage with these tools critically and ethically. In our 200-level Introduction to Microbiology course, we developed a series of scaffolded activities to promote digital literacy, responsible AI use, and critical evaluation of AI-generated content. Structured course activities allowed students to practice effective prompting, evaluate AI-created summaries, and identify inaccuracies and false references. Ultimately, this allowed students to generate and critique their own AI-assisted unit summaries for their course learning portfolios. Informal feedback from 100 students revealed that two-thirds of students felt the activities clarified the benefits and pitfalls of AI, while about half reported learning new information about the technology. The feedback emphasized that the assignments revealed AI's limitations and reinforced the need to verify its outputs. The activities also aligned with broader curricular goals around digital literacy and critical thinking. Overall, integrating structured AI-focused activities can empower students to apply AI responsibly, but with a critical lens of its limitations.
Digital pathology (DP) and artificial intelligence (AI) promise faster, more accurate cancer diagnostics, yet patient views remain undocumented. We explored perspectives of patient representatives on DP and AI implementation. A two-hour moderated roundtable with six Flemish cancer-patient advocates was recorded, transcribed and analyzed using reflexive thematic analysis. Participants anticipated improved accuracy, shorter turnaround times and stronger inter-laboratory collaboration. Trust in AI was high when algorithms were trained on diverse datasets and pathologists retained final responsibility. Clinical validity outweighed full algorithmic transparency, though ongoing explainability research was encouraged. Explicit mention of AI in reports was considered unnecessary if quality assurance was demonstrable. Privacy worries focused on potential insurer misuse rather than pseudonymized cloud transfer. Representatives requested future tools that translate technical reports into lay language and suggested questions to support shared decision-making. Patient representatives were generally supportive of the introduction of AI in pathology, provided that algorithms are clinically validated, trained on representative datasets, and deployed under clear professional oversight. Their comments specifically highlight expectations regarding human-AI collaboration, data governance, auditability, and communication about AI use. These AI-focused insights can help laboratories, vendors, and regulators align development and implementation with patient priorities.
This study explored nurses' perspectives on the adoption and utilization of artificial intelligence (AI) in clinical practice within a large university-affiliated health system in the southeastern United States. Through a survey enriched by open-ended questions, we captured the unique concerns and suggestions of nursing professionals regarding the deployment of AI technologies in a range of clinical settings. The majority of nurses have limited exposure to and experience with generative and predictive AI tools. In addition, they have concerns about the availability of related training opportunities, AI process integration, and ethical implications of AI implementation. There are critical workforce development needs and substantial opportunities for enhanced training to incorporate both ethical considerations and technical skills. This research illuminates the perspective and experience of nurses using AI. Specifically, it provides insights into the nursing workforce's readiness to adopt and utilize AI in clinical practice. This research also informs the integration of AI-focused curriculum and professional development for nurses. Specifically, more structured training is needed for nurses to use AI responsibly. Nurse administrators should be aware of the hesitations and concerns of this large population, as nurses are ultimately the front-line end users.
Health Research Ethics Committees (HRECs) play a pivotal role in safeguarding research participants and ensuring ethical conduct. The rapid integration of artificial intelligence (AI) into health research introduces novel ethical and operational complexities, including algorithmic opacity, bias, data governance challenges, and difficulties in post-approval monitoring. However, empirical evidence on how these AI-specific complexities affect HREC operations in Tanzania remains limited. This study explored operational challenges associated with ethical oversight of AI-related health research among HRECs in Tanzania. An exploratory qualitative study design was employed, involving 25 participants (15 HREC members and 10 secretariat staff) purposively selected from 10 HRECs across five zones of mainland Tanzania. In-depth interviews were conducted using a semi-structured guide. Data were transcribed, translated, and analyzed using inductive content analysis with thematic interpretation, supported by NVivo software. An audit trail, reflexive journaling, and data source triangulation were used to enhance credibility and trustworthiness. A total of 28 codes were generated and organized into 10 subthemes and four overarching themes. While general challenges such as limited funding, high workload, and staffing constraints persisted, AI-related protocols introduced additional operational burdens, including difficulties in assessing algorithmic validity, increased reliance on external technical experts, and challenges in reviewing large-scale datasets. Although 50% of secretariat staff had more than five years of experience, participants emphasized that the key limitation was not general experience but insufficient AI-specific technical expertise. Weak post-approval monitoring systems were particularly inadequate for tracking AI-driven studies. Tanzanian HRECs demonstrate foundational governance capacity but face AI-specific operational and technical challenges that constrain effective oversight. Strengthening AI-focused training, technical advisory mechanisms, digital review systems, and sustainable financing is essential to support the ethical governance of emerging technologies.
Artificial intelligence (AI)-enabled decision support systems are increasingly used in emergency departments and intensive care units to support triage, prediction of deterioration, sepsis recognition and escalation decisions. Although these tools may enhance patient safety, they also introduce new challenges for nurses who remain professionally accountable for clinical judgement in high-acuity settings. To explore emergency and critical care nurses' experiences of using AI-enabled decision support systems, with a focus on clinical judgement, escalation decisions, professional responsibility and ethical considerations across the emergency department-to-intensive care unit continuum. A qualitative study informed by Husserl's descriptive phenomenology was conducted across three emergency departments and three intensive care units in three public hospitals in northern Saudi Arabia. Semi-structured interviews were conducted with 16 nurses who had direct experience using AI-enabled decision support systems. Data were analysed using Braun and Clarke's reflexive thematic analysis. Reporting followed the Standards for Reporting Qualitative Research. Three themes were identified. 'Judgment under algorithmic pressure' reflected nurses' efforts to interpret AI alerts alongside bedside assessment, clinical uncertainty and concerns about over-reliance. 'Navigating escalation and responsibility' highlighted heightened accountability during patient deterioration and transfer from the emergency department to intensive care. 'Professional identity and ethical framing' captured nurses' concerns about role changes, moral agency and ethical discomfort when AI recommendations conflicted with patient-centred judgement. Emergency and critical care Nurses experienced AI-enabled decision support as both helpful and burdensome. Rather than reducing responsibility, AI intensified interpretive work, accountability and ethical tension. Human-centred governance, AI literacy and clear accountability frameworks are needed to support safe AI integration in acute care. Nurses require organisational support, ethical guidance and AI-focused education to critically engage with decision support systems while preserving professional judgement and moral agency.
IntroductionArtificial intelligence (AI) is increasingly applied in prostate cancer screening and diagnostic evaluation; however, the structure, methodological characteristics, and clinical positioning of AI-focused trials remain incompletely characterized. This study aimed to map the clinical trial landscape of AI applications in prostate cancer diagnosis using registry-based evidence mapping.MethodsA registry-based evidence-mapping analysis was conducted using ClinicalTrials.gov. Trials registered up to 15 November 2025 were systematically identified using search terms related to prostate cancer and AI-based methodologies. Eligible studies included interventional and observational trials evaluating AI applications for diagnostic purposes. Data were extracted on study design, diagnostic modality, functional role of AI, comparator framework, and validation strategy. Descriptive statistics and cross-tabulation analyses were used to characterize patterns across studies. The study selection process was presented using a PRISMA-style flow diagram.ResultsA total of 84 trials met the inclusion criteria. Imaging-based AI applications predominated, accounting for 52.4% of studies, with magnetic resonance imaging (MRI) representing the most frequently investigated modality (34.5%). Biomarker-based (16.7%), multimodal (15.5%), and computational pathology (7.1%) approaches were less frequently reported. The most common functional applications were classification and risk prediction (48.8%) and lesion detection and segmentation (29.8%). Most studies employed prospective observational designs (84.5%) and frequently relied on stand-alone AI evaluation frameworks (39.2%). Histopathology or biopsy confirmation was the most commonly reported reference standard (56.0%). Only a limited number of trials incorporated workflow integration or clinical decision-support evaluation.ConclusionAI research in prostate cancer diagnostics appears to be primarily centered on imaging-based, early-phase, and performance-oriented studies. Current evidence suggests that AI systems are predominantly positioned as decision-support tools rather than fully integrated clinical solutions. Greater emphasis on multicenter validation, standardized reporting, and clinically relevant outcome evaluation may be required to support broader clinical implementation.
Artificial Intelligence (AI) has the potential to revolutionize healthcare by enhancing diagnostic accuracy, streamlining administrative tasks, and improving patient care. However, the integration of AI into clinical practice faces significant barriers, particularly in nursing. Nurses, like frontline healthcare providers, are uniquely positioned to observe and experience these challenges. Understanding their perspectives can offer valuable insights into the obstacles hindering AI adoption in nursing practice, especially within acute care settings. This study aimed to explore the perceptions of nurses regarding the barriers to integrating AI technologies into clinical practice. A qualitative design. A qualitative phenomenological approach was employed to capture nurses lived experiences. Twenty nurses were recruited through purposive sampling from 20 healthcare institutions (eight tertiary care hospitals, eleven general hospitals, and one district hospital) in Dhaka, Bangladesh. Data were collected over a three-month period (April to June 2025) using semi-structured interviews and focus group discussions. Transcripts were analyzed using van Manen's reflective methodology. The conceptual framework incorporated the Technology Acceptance Model, emphasizing perceived usefulness and ease of use, while also considering the cultural and infrastructural context. The study identified three major themes: (i) knowledge and awareness gaps, (ii) organizational barriers, and (iii) ethical and interpersonal concerns. Nurses highlighted a lack of AI-focused education, inadequate institutional support, and concerns about privacy and dehumanization of care. Misconceptions about AI's capabilities and exclusion from AI-related decision-making processes further contributed to resistance and skepticism. The study underscores the need for targeted reforms in nursing education to include comprehensive AI training. Addressing organizational and ethical barriers, such as providing adequate resources, robust privacy measures, and inclusive policies, is crucial. By empowering nurses and fostering interdisciplinary collaboration, healthcare systems can leverage AI effectively while preserving human-centered care.
Background/Objectives: Digital technologies are increasingly used in interventions for children with Autism Spectrum Disorder (ASD) to support language development. However, existing evidence remains fragmented due to heterogeneity in intervention types, participant characteristics, and outcome measures. This systematic review aims to synthesize current empirical findings on the effects of digital interventions on language development in children with ASD and to identify key factors influencing intervention effectiveness. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. Searches were performed in PubMed, Scopus, Web of Science, ERIC, and PsycINFO for studies published between 2010 and 2025. Eligible studies included experimental, quasi-experimental, and intervention-based designs involving children aged 2-18 years with ASD and reporting at least one language-related outcome. Data extraction was performed independently by two reviewers using a structured form. Methodological quality was assessed using the Joanna Briggs Institute (JBI) checklist and CASP tools. Due to heterogeneity across studies, a narrative synthesis approach was applied. Results: A total of 61 studies met the inclusion criteria. Findings indicate that digital interventions generally have positive effects on language development in children with ASD, with stronger and more consistent outcomes in receptive and expressive language domains. Intervention effectiveness varied according to duration, intensity, content quality, and contextual factors such as family involvement and technological access. Conclusions: The evidence suggests that digital interventions may have positive effects on language development in children with ASD, particularly in receptive and expressive language domains. Among intervention types, video modeling and AI-supported approaches appear to show promising outcomes; however, these findings should be interpreted with caution due to the limited number of AI-focused studies and substantial heterogeneity in study designs, sample characteristics, and outcome measures. Gamified and mobile applications demonstrate moderate effects, especially in vocabulary and pragmatic language skills. Overall, intervention effectiveness varies according to duration, intensity, content quality, and contextual factors such as family involvement and technological access. Future research should prioritize standardized methodologies and longitudinal designs.
Background and aim Artificial intelligence (AI) has become an indispensable component of medical practice. Studies have shown that medical students worldwide are often ill-prepared to take full advantage of these technologies in their future practice due to inadequate training and a shortage of qualified instructors. These factors hinder the adoption of AI-focused curricula; therefore, this study aims to explore medical students' and faculty's knowledge and attitudes toward AI applications in the medical field, as well as their readiness for its integration into the medical curriculum, highlighting both the benefits and barriers. Methods A cross-sectional study was conducted on medical students (years 1-5) and faculty members from the University of Sharjah College of Medicine (UOSCOM), Sharjah, UAE. They were recruited via convenience sampling between February and March 2023. Students and faculty completed self-administered questionnaires, both online and in person. Analysis was performed using IBM SPSS Statistics for Windows, Version 26 (Released 2018; IBM Corp., Armonk, NY, USA). Results Of a total of 413 participants, 74.3% (faculty and students) agreed that the medical field has benefited from AI. Interestingly, 54.3% of students agreed on implementing AI in the curriculum, and 79.3% believe it is necessary, as they see it as a way to empower their careers in the future. However, 51.9% expressed concerns regarding the increase in workload. Of the 45 faculty participants, 66.7% believe that teaching AI would be an addition to their CVs, while 40% disagree that adding the course will increase their workload. Moreover, 77.8% are willing to participate in the course. Conclusions and recommendations The findings revealed a general positive perception, with both groups acknowledging the benefits of AI in medicine. Students show awareness of AI applications in education and healthcare, while faculty express willingness to teach AI courses for professional development. However, we recommend that future studies assess students' baseline computer knowledge and correlate faculty members' specialty fields with their willingness to teach.
Artificial intelligence (AI) is transforming the character of warfare through autonomous systems, real-time data analytics, and algorithmic decision-making, creating new operational, ethical, and clinical challenges for military medicine. While modern battlefields become increasingly shaped by AI-enhanced targeting, autonomous weapons, and contested digital environments, current military medical education and doctrine have not evolved to address these developing threats. This commentary outlines the systemic vulnerabilities that limit military medicine's readiness for AI-enabled large-scale combat operations, including infrastructure gaps, data integrity challenges, cybersecurity threats, and a lack of doctrinal and educational alignment with the evolving battlespace. Drawing from recent conflicts and emerging technologies, we identify critical gaps in trauma training, medical logistics, and ethical preparedness and offers concrete recommendations for reform. Our education and training recommendations include embedding AI-focused scenarios into high-fidelity simulation exercises, training medical personnel in human-AI teaming, and emphasizing data stewardship as a key clinical competency. Through targeted curricular redesign, ethical education, and interdisciplinary collaboration, military medicine can adapt to the demands of AI-driven warfare and ensure operational readiness in future conflicts.
Advances in artificial intelligence (AI) have revolutionized digital wellness by providing innovative solutions for health, social connectivity, and overall well-being. Despite these advancements, the older population often struggles with barriers such as accessibility, digital literacy, and infrastructure limitations, leaving them at risk of digital exclusion. These challenges underscore the critical need for tailored AI-driven interventions to bridge the digital divide and enhance the inclusion of older adults in the digital ecosystem. This study presents a comparative bibliometric analysis of research on the role of AI in promoting digital wellness, with a particular emphasis on the older population in comparison to the general population. The analysis addressed five key research topics: (1) the evolution of AI's impact on digital wellness over time for both the older and general population, (2) patterns of collaboration globally, (3) leading institutions' contribution to AI-focused research, (4) prominent journals in the field, and (5) emerging themes and trends in AI-related research. Data were collected from the Web of Science between 2016 and 2025, totaling 3429 documents (344 related to older people), analyzed using bibliometric tools. Results indicate that AI-related digital wellness research for the general population has experienced exponential growth since 2016, with significant contributions from the United States, the United Kingdom, and China. In contrast, research on older people has seen slower growth, with more localized collaboration networks and a steady increase in citations. Key research topics for the general population include digital health, machine learning, and telemedicine, whereas studies on older people focus on dementia, mobile health, and risk management. The results of our analysis highlight an increasing body of research focused on AI-driven solutions intended to improve the digital wellness among older people and identify future research directions to refer to the specific needs of this population segment.