Background: Artificial intelligence (AI) is increasingly shaping medical education through adaptive learning systems, simulations, and large language models. These tools can enhance knowledge retention, clinical reasoning, and feedback, while raising concerns related to equity, bias, and institutional readiness. Methods: This narrative review examined AI applications in medical and health-profession education. A structured search of PubMed, Scopus, and Web of Science (2010–October 2025), supplemented by grey literature, identified empirical studies, reviews, and policy documents addressing AI-supported instruction, simulation, communication, procedural skills, assessment, or faculty development. Non-educational clinical AI studies were excluded. Results: AI facilitates personalized and interactive learning, improving clinical reasoning, communication practice, and simulation-based training. However, linguistic bias in Natural language processing (NLP) tools may disadvantage non-native English speakers, and limited digital infrastructure hinders adoption in rural or low-resource settings. When designed inclusively, AI can amplify accessibility for learners with disabilities. Faculty and students commonly report low confidence and infrequent use of AI tools, yet most support structured training to build competence. Conclusions: AI can shift medical education toward more adaptive, learner-centered models. Effective adoption requires addressing bias, ensuring equitable access, strengthening infrastructure, and supporting faculty development. Clear governance policies are essential for safe and ethical integration.

The present study analyzed the characteristics of phase IV clinical trials in oncology using data from the ClinicalTrials.gov registry. The included trials were conducted between January 2013 and December 2022 and were examined for key characteristics, including outcome measures, interventions, sample sizes, and study design, different cancer types, and geographic regions. The analysis included 368 phase IV oncology studies. An amount of 50% of these studies examined both safety and efficacy, while 43.5% only reported efficacy outcome measures, and 6.5% only described safety outcome measures. Only 16.9% of studies were powered to detect adverse events with a frequency of 1 in 100. Targeted therapies accounted for the majority of included studies (53.5%), with breast (32.91%) and hematological cancers (25.82%) being the most frequently investigated malignancies. Most phase IV oncology studies lacked sufficient power to detect rare adverse events due to their small sample sizes and instead focused on effectiveness. To ensure that there is no gap in drug safety data collection and detection of rare adverse events due to limited phase IV clinical trials, there is a significant need for additional education and participation by both health care providers and patients in spontaneous reporting processes.

Atrial fibrillation (AF) is a common cardiac arrhythmia linked to an increased risk of stroke, heart failure, and mortality, yet its diagnosis remains challenging due to its intermittent and often asymptomatic nature. Traditional methods, such as standard electrocardiography (ECG) and prolonged cardiac monitoring, have limitations in terms of cost, accessibility, and diagnostic yield. Artificial intelligence (AI), particularly machine learning (ML) and deep learning, has emerged as a promising tool for AF detection and prediction by analyzing ECG data with high accuracy. AI models can identify subtle patterns in ECG signals that may indicate AF, even when the arrhythmia is not actively present, improving early diagnosis and risk stratification. Additionally, AI-powered ECG analysis has been integrated into wearable and mobile health devices, expanding screening capabilities beyond clinical settings. While studies have demonstrated AI’s effectiveness, challenges such as data bias, model reliability across diverse populations, and regulatory considerations must be addressed before widespread clinical adoption. If these obstacles are overcome, AI has the potential to revolutionize AF management by enabling earlier detection, reducing the need for resource-intensive monitoring, and improving patient outcomes.

Background: The anatomical characteristics of the anterior ethmoidal artery (AEA) exhibit a high degree of variability, especially regarding its topography. Methods: PubMed, Scopus, Embase, Web of Science, Cochrane Library, and Google Scholar were searched to identify all studies that included information regarding the morphometric and topographical characteristics of the AEA. Results: Ultimately, a compilation of 38 studies meeting the predetermined criteria, and possessing comprehensive and pertinent data, were incorporated into the current meta-analysis. In the overall analysis, reports of the AEA being embedded within the skull base or at the level of the skull base producing a bony protrusion were found in 56.91% of cases (95% CI: 43.55–69.80%). The mean diameters of the AEA in the intraorbital and intracranial areas were 0.94 mm and 0.76 mm, respectively. Moreover, the artery coursed most commonly between the second and third lamellae, with a prevalence of 65.82% (95% CI: 55.39–75.56%). Conclusions: The AEA may be at a high risk of iatrogenic injury during various sinus and skull base surgeries, especially if the surgeon performing these procedures is unfamiliar with the vast number of variations this vessel may exhibit. The spatial relationship between this vessel and the skull base is especially variable, and understanding this variability goes hand in hand with intraoperative safety.

Background: Out-of-hospital cardiac arrest (OHCA) remains a major public health challenge across Europe. While prehospital emergency medical services (EMS) vary widely, the degree to which regional infrastructure, dispatch guidance, and community responder frameworks influence clinical outcomes when mapped against standardized reporting templates remains insufficiently synthe-sized. This scoping review evaluates European OHCA evidence across standardized Utstein re-porting domains to analyze prehospital system factors, responder interventions, and clinical out-comes. Methods: Following PRISMA-ScR guidelines and Joanna Briggs Institute (JBI) methodology, a systematic literature search was performed across PubMed, Scopus, and Web of Science (January 2021–March 2026). From 8,882 records identified, 2,126 full-text articles were evaluated using a Population, Exposure, Outcome (PEO) framework. Data were synthesized across four core Utstein domains. Results: Forty three primary studies (N = 43) were included. System Factors demonstrated that prolonged ambulance response intervals severely diminish survival, particularly in sparsely populated rural sectors. Dispatch Protocols highlighted that rapid dispatcher recognition signifi-cantly increases telephone CPR instructions and community resource activation. Under bystander a prominent gap was identified between public AED availability and actual lay bystander deployment (< 5%). Dispatched Volunteer Community Responders (VCR) and First Responders (FR) achieved median arrival time-gains of 4–5 minutes; however, survival benefits were descriptively observed primarily when early arrival facilitated ultra-early defibrillation or high-quality CPR. Clinical Out-comes confirmed that 30-day survival and favorable neurological recovery (CPC 1–2) remain highly variable, heavily constrained by reporting heterogeneity. Conclusions: Strengthening early links in the chain of survival—specifically dispatcher recognition, app-alerted volunteer dispatch, and public defibrillation—is consistently associated with improved clinical survival. Universal adoption of the updated Utstein 2024 reporting template is essential to eliminate reporting heterogeneity and enable meaningful cross-system benchmarks across Europe.

Our research analyzed the characteristics of phase IV clinical trials in oncology using data from the ClinicalTrials.gov registry. The included trials were conducted between January 2013 and December 2022 and were examined for key characteristics, including outcome measures, interventions, sample sizes, and study design, different cancer types, and geographic regions. Our analysis included 368 phase IV oncology studies. We found that 50% of these studies aimed to examine both safety and efficacy, while 43.5% only reported efficacy outcome measures, and 6.5% only described safety outcome measures. Only 16.9% of studies were powered to detect adverse events with a frequency of 1 in 100. Targeted therapies accounted for the majority of included studies (53.5%), with breast (32.91%) and hematological cancers (25.82%) being the most frequently investigated malignancies. Most phase IV oncology studies lacked sufficient power to detect rare adverse events due to their small sample sizes and instead focused on effectiveness. To ensure that there is no gap in drug safety data collection and detection of rare adverse events due to limited phase IV clinical trials, there is a significant need for additional education and participations by both health care providers and patients in spontaneous reporting processes.