Background: Generative artificial intelligence (AI), particularly large language models (LLMs), is rapidly transforming medical education. These tools can act as virtual tutors, generate teaching materials and support clinical simulations, but they also raise concerns regarding accuracy, bias, academic integrity, data protection and professional identity formation. Objective: This narrative review aims to synthesise current knowledge on the use of generative AI - with a focus on LLMs - in medical education, identify key benefits and risks, and discuss implications for curricula and professionalism in medicine and other health professions. Methods: Publications from 2021–2025 were included if they addressed applications of generative AI/LLMs in medical or health professions education, or discussed their impact on curricula, assessment, professionalism or future physician competencies. Original empirical studies, reviews, conceptual papers and policy/guideline documents were included. Data were extracted and synthesised narratively according to four domains: areas of application, benefits, risks/limitations and recommendations/frameworks. Results: Generative AI is being used to support self-directed learning (on-demand explanations, personalised practice questions), clinical simulations and virtual patients, faculty work in content generation and assessment, and research in medical education. Reported benefits include increased accessibility, personalisation, scalability and opportunities to practise clinical reasoning and communication in low-stakes environments. Major risks comprise hallucinations and bias, threats to academic integrity, potential deskilling and over-reliance, and privacy and confidentiality concerns.

Cannabinoids derived from Cannabis sativa L., particularly cannabidiol (CBD) and minor cannabinoids such as cannabigerol (CBG), cannabichromene (CBC), tetrahydrocannabivarin (THCV), and cannabidivarin (CBDV), have attracted increasing interest due to their potential therapeutic properties in dermatology. These compounds exert anti-inflammatory, antimicrobial, immunomodulatory, sebostatic, and antiproliferative effects, which may benefit a range of skin conditions. Preclinical and early clinical data suggest that cannabinoids may effectively alleviate inflammation, pain, pruritus, and microbial infections in various dermatologic disorders. Topical and transdermal routes show promise due to improved safety and targeted delivery. CBD has demonstrated efficacy in reducing sebum production in acne, modulating immune responses in psoriasis, and accelerating wound healing in EB. However, findings for alopecia areata remain inconsistent. Emerging delivery systems, including CBG-based emulgels and CBD transdermal patches, may improve local bioavailability and clinical outcomes. Cannabinoids, especially CBD, exhibit significant potential as adjuvant or alternative treatments for inflammatory, infectious, and autoimmune skin diseases. Despite promising preliminary results, large-scale, randomized, placebo-controlled trials are needed to establish optimal dosing, formulations, safety, and long-term efficacy in clinical dermatology.

Introduction and Purpose: Mental health disorders such as depression, anxiety, and stress-related conditions are major contributors to global disability. Growing evidence indicates that physical activity is an effective non-pharmacological strategy for preventing and managing these conditions. This review aims to summarize current knowledge on the impact of physical activity on mental health and the mechanisms through which it exerts its effects. Material and Method: A narrative review was conducted using PubMed, Scopus, and Google Scholar to identify studies from 2010-2025 examining physical activity and mental health outcomes in adults. Priority was given to randomized trials, systematic reviews, and meta-analyses exploring exercise-based interventions and their psychological or physiological effects. Results: The analyzed literature shows that regular physical activity - particularly aerobic and moderate-intensity exercise - supports mood regulation, enhances cognitive functioning, and increases resilience to stress. Reported mechanisms include neurobiological changes such as improved neurotransmitter balance, reduced inflammation, and enhanced neuroplasticity, alongside psychological benefits such as increased self-efficacy and reduced rumination. Structured exercise programs also demonstrate efficacy as adjuncts to psychotherapy and pharmacotherapy. Conclusions: Physical activity represents a valuable and evidence-based component of mental health promotion and treatment. Incorporating tailored exercise interventions into prevention strategies and clinical care may significantly improve psychological well-being and reduce the burden of mental health disorders

Background: This review provides a critical analysis of selected clinical assessment instruments for depression, with emphasis on their utility in everyday medical practice and their adaptability to individual patient profiles. Given the multifactorial etiology and diverse symptomatology of depressive disorders, appropriate diagnostic tool selection is essential for accurate evaluation and monitoring. Aim: The paper reviews widely used depression assessment scales, including the Hamilton Depression Rating Scale (HDRS), Montgomery-Åsberg Depression Rating Scale (MADRS), Beck Depression Inventory (BDI), Patient Health Questionnaire (PHQ), Zung Self-Rating Depression Scale (ZSDS), Center for Epidemiologic Studies Depression Scale (CES-D), Depression Anxiety Stress Scales (DASS), and others, analyzing their relevance to various clinical populations, including athletes. Materials and Methods: A structured literature review was conducted using PubMed and Web of Science databases. Keywords included: major depressive disorder, psychometric assessment, screening tools, diagnostic evaluation, and mood disorders. Results: The examined tools differ in structure, scoring methods, and clinical applicability. Their utility varies depending on age, comorbidities, and context. Some instruments are better suited for older adults, postpartum women, or adolescents, while others may offer better sensitivity in athletic populations. Matching the tool to the individual's clinical and psychosocial profile improves diagnostic accuracy and treatment outcomes. Conclusions: Selecting depression assessment scales based on psychometric quality and population specificity—particularly in underrepresented groups such as athletes—can optimize diagnosis and facilitate effective, personalized treatment strategies.

Background. Musculoskeletal injuries are a common consequence of recreational physical activity. In some cases, they may present as acute pain syndromes with a non-specific clinical presentation that can mimic symptoms of internal diseases. Objective. The aim of this study was to present the most common musculoskeletal injuries occurring in recreational athletes and to discuss their relevance in the differential diagnosis of acute pain syndromes. Methods. A narrative review of the literature was conducted using the PubMed, Scopus, and Google Scholar databases. Publications addressing musculoskeletal injuries in physically active individuals and their clinical presentation as acute pain were analyzed. Results. The analyzed data indicate that injuries involving the lumbar region, abdominal wall, groin, and pelvis represent some of the most frequent causes of acute pain among recreational athletes. The clinical manifestations of these injuries may resemble renal colic, acute abdominal conditions, or inguinal pathologies, which can pose significant diagnostic challenges in clinical practice. Conclusions. The analyzed data indicate that injuries involving the lumbar region, abdominal wall, groin, and pelvic area are among the most common causes of acute pain in recreational athletes. The symptoms of these injuries may clinically resemble renal colic, acute abdominal conditions, or groin pathologies, which may lead to diagnostic difficulties.

Background and Objectives: Primary myelofibrosis (PMF) is a rare hematologic malignancy with non-specific symptoms, causing diagnostic delays and missed diagnoses. Automated screening in heterogeneous electronic health records (EHRs) is challenging due to class imbalance, data sparsity, and incomplete labeling. We investigated two complementary objectives: (1) developing a screening algorithm using routine EHR data to identify PMF-risk patients for hematology consultation, and (2) assessing the applicability of positive-unlabeled (PU) learning. Methods: Using EHR data from 10 Polish hospitals, we evaluated several ensemble models and found that LightGBM achieved the best performance. Results: LightGBM achieved AP 20.83% (95% CI: 19.18–24.35%), sensitivity 45.52% (95% CI: 39.48–52.72%), and precision 14.72% (95% CI: 13.80–17.01%)—substantially exceeding expected prevalence (over 340-fold enrichment). The model captured interactions among RDW and PLT parameters revealing high-risk unlabeled patients with confirmed diagnoses (n = 5) or clinical profiles resembling PMF cases (n = 31), confirming PU problem. Conclusions: Although PU methods enhanced sensitivity, they reduced precision to levels exceeding real-world healthcare capacity. Ensemble learning enables disease identification in clinical settings.