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 objective of this meta-analysis was to present transparent data on the morphology of the pituitary gland (PG) using the available data in the literature. The main online medical databases, such as PubMed, Embase, Scopus, and Web of Science, were searched to gather all relevant studies regarding PG morphology. The mean overall volume of the PG was found to be 597.23 mm³ (SE = 28.81). The mean overall height of the PG was established to be 5.64 mm (SE = 0.11). The mean overall length of the PG was found to be 9.98 mm (SE = 0.26). In the present study, the PG’s overall morphology and morphometric features were analyzed. Our results showed that, on average, females from Asia have the highest volume of PG (706.69 mm³), and males from Europe have the lowest (456.42 mm³). These values are crucial to be aware of because they represent the normal average properties of the PG, which may be used as reference points when trying to diagnose potential pathologies of this gland. Furthermore, the present study’s results prove how the PG’s size decreases with age. The results of the present study may be helpful for physicians, especially surgeons, performing procedures on the PG.

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.

Actinotignum schaalii (A. schaalii) is a Gram-positive, anaerobic bacterium. While primarily associated with urinary tract infections in elderly and immunocompromised individuals, it has also been implicated in soft tissue infections such as necrotizing fasciitis. However, its role as a primary pathogen in necrotizing fasciitis outside the genitourinary region remains underreported. We present a case of necrotizing fasciitis of the axilla caused by A. schaalii in an elderly diabetic female. This case is unique in that it highlights the bacterium’s role as the sole causative agent in a severe soft tissue infection, diverging from its typical polymicrobial involvement. This case emphasizes the pathogenic potential of A. schaalii in severe soft tissue infections and highlights the importance of advanced microbiological techniques for its accurate identification.

Background: Gastroesophageal reflux disease remains a common postoperative issue after sleeve gastrectomy. This study examined whether preoperative symptoms, clinical factors, and endoscopic findings predict reflux symptoms one year after surgery. Methods: We performed an observational cohort study including 207 patients who underwent sleeve gastrectomy (SG). Preoperative variables included age, sex, BMI, comorbidities, preexisting GERD symptoms, and esophagogastroduodenoscopy (EGD) results. GERD symptoms were evaluated at 1 year. Multivariable logistic regression identified factors associated with GERD after surgery, GERD resolution, and de novo GERD. Results: One year after SG, the prevalence of GERD increased from 45% to 60% (p = 0.003). Preexisting GERD symptoms (OR 2.54 (95% CI 1.39–4.63, p = 0.002) and lower preoperative BMI (OR 0.93; 95% CI 0.88–0.99; p = 0.027) were independently linked to GERD after surgery. Among patients with preoperative GERD, higher preoperative BMI was associated with symptom resolution (OR 1.11; 95% CI 1.03–1.21; p = 0.011). Neither Hill classification nor mild-to-moderate (Los Angeles grade A/B) esophagitis on EGD predicted GERD outcomes. Conclusions: Preexisting GERD symptoms and BMI were the most reliable predictors of GERD after surgery. Preoperative Hill classification and esophagitis showed no significant association in this cohort.

Background: Transthyretin amyloid cardiomyopathy (ATTR-CM) is a progressive infiltrative cardiomyopathy associated with substantial morbidity and mortality. This systematic review evaluates the effects of tafamidis on echocardiographic parameters, cardiac biomarkers, functional outcomes, mortality, and safety in patients with ATTR-CM. Methods: This systematic review was conducted in accordance with PRISMA 2020 guidelines and registered in PROSPERO (CRD420261391553). PubMed, Embase, and Web of Science were searched through 20 March 2026. Eligible studies included adults with ATTR-CM treated with tafamidis and reporting, echocardiographic, biomarker, functional, mortality, or safety outcomes. Randomized controlled trials and observational studies were included. Risk of bias was assessed using the Cochrane RoB 2 tool and Newcastle–Ottawa Scale. Due to substantial heterogeneity, a narrative synthesis was performed. Results: Seventeen studies were included with a total of 1890 patients. Tafamidis treatment was potentially associated with stabilization of global longitudinal strain, preservation of functional status, and lower all-cause mortality compared with untreated or control cohorts. Biomarker findings, including N-terminal pro-B-type natriuretic peptide (NT-proBNP) and high-sensitivity cardiac troponin T (hs-cTnT), were heterogeneous and did not demonstrate a consistent pattern of improvement. Functional outcomes, including New York Heart Association (NYHA) class, 6-minute walk distance (6MWT), National Amyloidosis Center (NAC) staging, and quality-of-life measures, suggested slower clinical deterioration among treated patients. Limited available safety data indicated that tafamidis was generally well tolerated, with no major safety concerns identified. Conclusions: Current evidence may suggest that tafamidis slows disease progression and may potentially be associated with improved survival in ATTR-CM. Further prospective studies with standardized outcome reporting are needed. Evidence was limited by heterogeneity in study design, outcome reporting, and follow-up duration, with most included studies being observational.