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.

A multi-cohort evaluation of a curriculum-aligned, oncology-focused chatbot in pharmacy education.

Artificial intelligence in sleep medicine I: Diagnosis, treatment, care, and research.

Digital readiness in nursing education: eHealth literacy, AI attitudes, and associated factors among undergraduate students.

AI disclosure uncertainty in nursing education: A pedagogical scaffold for professional learning.

Green artificial intelligence in health applications.

The intelligent ear: AI and hearing aids information seeking and users' discussion on social media.

Is it time for AI to take a leading role in mass trauma mental health response?

From decision support to clinical integration: A scoping review of artificial intelligence in prehospital airway management.

Sports-related knee injuries are common and debilitating, often leading to chronic pain, early osteoarthritis, and reduced performance. Artificial Intelligence (AI) has emerged as a promising tool to improve their prevention, diagnosis, prognosis, and rehabilitation. This review summarises current evidence on the clinical applications, limitations, and future directions of AI and machine learning in sports-related knee injuries. A narrative review of PubMed, Embase, Medline and Web of Science was conducted, examining recent literature on AI-based models in musculoskeletal and sports medicine. The review was categorised into key domains: injury prediction and prevention, diagnostic imaging performance, AI-enabled clinical workflows, alongside postoperative and rehabilitation outcome modelling. AI algorithms demonstrate strong potential across the sports knee injury continuum. Predictive models analysing biomechanical and physiological data have achieved high area under the curve (AUC) values, in some cases above 0.90, in experimental and pilot setting when identifying athletes at risk of ACL rupture or overuse injuries, while machine learning approaches have been used to predict graft failure, revision surgery, and return-to-sport. However, most remain investigational rather than clinically deployable, with limited explainability, insufficient external validation, and training datasets that are often narrow or unrepresentative of broader athletic populations. AI has the potential to transform the management of sports-related knee injuries through more predictive, personalised, and precise care. However, wider clinical adoption will require multicentre validation, improved interpretability, and robust ethical and regulatory oversight. With further development, AI may enhance injury prevention, recovery, and improve long-term joint health outcomes in athletes.

Recent advancements in digital technologies and the integration of artificial intelligence (AI) with software systems have introduced new challenges in cybersecurity. Traditional frameworks such as MITRE ATT&CK have proven expressive enough for the analysis of software threats, yet they are limited in accommodating the vulnerabilities of ML systems. In response, MITRE ATLAS was developed to extend the threat analysis specifically to AI and machine learning (ML) environments, providing a structured taxonomy for adversarial tactics and techniques attempting to compromise them. In this paper, we extend the conversation by reviewing papers related to adversarial attacks and examining their categorization, their theoretical foundations, and their advancements compared to prior work. Specifically, we analyze a total of 63 papers across the entire AI attack spectrum and further delve into their objectives, threat models, scientific advancements, and evaluation. Our contributions include the first, to-date analysis of attack vectors following the MITRE ATLAS paradigm, a synthesis of recent advancements, and a discussion on the limitations in the current body of knowledge. We hope that our analysis clarifies the present challenges and serves as a foundation for future research towards securing AI systems. • Structured analysis using MITRE ATLAS to classify AI threats across six attack families, mapping tactics and techniques. • Systematic analysis of 63 methods across domains, evaluating theory, threat models, datasets, and results. • Actionable defense strategies mapping 24 mitigation mechanisms to MITRE ATLAS techniques as a practical guide for practitioners. • Synthesis of findings highlighting research gaps, future directions, and proposed enhancements to the MITRE ATLAS framework.

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.

Artificial intelligence language models for medical text analysis: A systematic review.

Process mining analyzes process execution data to derive insights that support operational process improvement. However, event logs often suffer from poor data quality, typically resulting from process deficiencies, which can lead to inaccurate or misleading insights. To mitigate this risk, domain experts and process analysts engage in data validation during event data preparation to assess whether an event log is fit for its intended analytical purpose. Yet, current practices often fail to sufficiently align event logs with their analytical objectives, commonly formalized as analysis questions. This misalignment impedes the detection of data quality issues, which frequently vary across application domains and analytical contexts. Generative artificial intelligence offers promising capabilities in this regard, including adaptability to diverse contexts, the ability to interpret complex data, and the generation of context-aware recommendations. To leverage this potential, we adopt the Design Science Research paradigm to iteratively develop Artificial Intelligence-Assisted Data Validation For Domain Experts (AID4DE) that integrates domain knowledge — rooted in experts’ practical engagement with operational processes — with generative artificial intelligence support to facilitate interaction with complex event log data. We instantiate AID4DE as an open-source software prototype and evaluate it through a three-phase approach: a competing artifact analysis, 14 semi-structured expert interviews, and a user study involving 18 information systems researchers. Our results show that AID4DE is both applicable and effective in supporting domain experts in data validation, enabling the systematic externalization of domain knowledge and rigorous assessment of event log’s fitness for purpose. • Improved data validation for domain experts through artificial intelligence support. • Artificial intelligence derives event log understanding from semantic visual analysis. • Contextual guidance improves experts’ understanding and validation of event log data. • Instantiated prototype evaluated as useful and applicable in a real-world setting. • Artificial intelligence and domain knowledge support fitness for purpose evaluation.

Can Artificial Intelligence Replicate Human Qualitative Analysis?

Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems.

Exploring the role of artificial intelligence in anterior cruciate ligament injuries in high-performing & elite athletes.

• Multidimensional taxonomy organizes XAI methods for drug discovery decision stages. • Critical assessment of interpretability metrics for chemical and biological validity. • Task-driven guidance for selecting XAI tools across the discovery pipeline. • Insight into emerging causal, multimodal, and knowledge-grounded explainability. • Identifies key open challenges to advance interpretable AI in molecular modeling. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), is accelerating drug discovery by enhancing target identification, virtual screening, absorption, distribution, metabolism, excretion, and toxicity (ADMET), and lead optimization. The drug development lifecycle increasingly benefits from data-driven decision support enabled by these approaches. However, the opaque nature of deep models limits their adoption in pharmaceutical contexts, where interpretability is critical for compound prioritization, toxicity evaluation, and regulatory acceptance. Explainable AI (XAI) aims to bridge this gap by providing human-understandable explanations (interpretability) that support hypothesis generation and mechanistically plausible, testable rationales, while explicitly requiring subsequent experimental validation. This review introduces a novel multidimensional taxonomy of XAI approaches tailored to drug discovery, where methods are organized by input modality, degree of model transparency, and interpretability objectives, providing a task-centered framework for method selection across specific decision stages. We critically analyze core techniques including SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), saliency maps, attention mechanisms, surrogate models, counterfactuals, and causal inference, together with evaluation metrics such as fidelity, stability, sparsity, and interpretability. In contrast to prior overviews largely centered on specific applications, our work emphasizes mechanistic plausibility, alignment with decision-making needs, and emerging hybrid frameworks that integrate symbolic reasoning with multimodal data to promote interpretability grounded in chemical and biological knowledge. By integrating method-agnostic tools with quantitative evaluation schemes and decision-focused case studies, this survey offers a structured roadmap for deploying XAI in drug discovery and advances the discussion on interpretable AI in high-stakes scientific domains. We further examine the limitations of current XAI approaches, including documented failure modes such as shortcut learning and Clever Hans–type effects, and analyze practical barriers that constrain the translation of XAI into real-world drug discovery workflows.

"Perceptions of artificial intelligence in nursing students: A qualitative meta-synthesis based on the UTAUT2 model".

Artificial intelligence: Catalyzing a new era in pancreatic cancer cure.