Atherosclerosis, which currently contributes to 31% of deaths globally, is of critical cardiovascular concern. Current diagnostic tools and biomarkers are limited, emphasizing the need for early detection. Lifestyle modifications and medications form the basis of treatment, and emerging therapies such as photodynamic therapy are being developed. Photodynamic therapy involves a photosensitizer selectively targeting components of atherosclerotic plaques. When activated by specific light wavelengths, it induces localized oxidative stress aiming to stabilize plaques and reduce inflammation. The key advantage lies in its selective targeting, sparing healthy tissues. While preclinical studies are encouraging, ongoing research and clinical trials are crucial for optimizing protocols and ensuring long-term safety and efficacy. The potential combination with other therapies makes photodynamic therapy a versatile and promising avenue for addressing atherosclerosis and associated cardiovascular disease. The investigations underscore the possibility of utilizing photodynamic therapy as a valuable treatment choice for atherosclerosis. As advancements in research continue, photodynamic therapy might become more seamlessly incorporated into clinical approaches for managing atherosclerosis, providing a blend of efficacy and limited invasiveness.

Background: Artificial intelligence (AI) is rapidly transforming clinical medicine by enabling earlier disease detection, personalized risk stratification, precision diagnostics, and optimized therapeutic decision-making across multiple specialties. Methods: This narrative review synthesizes the most recent evidence from prospective randomized controlled trials, large cohort studies, and real-world implementations of AI in cardiology, pulmonology, neurology, hepatology, pancreatic diseases, and other key areas of internal medicine. Studies were selected based on clinical impact, external validation, and regulatory approval status where applicable. Results: AI systems now outperform traditional clinical tools in numerous high-stakes applications: >88% freedom from atrial fibrillation at 1 year with AI-guided ablation, noninferior stent optimization versus OCT guidance, >95% sensitivity for atrial fibrillation and low ejection fraction detection on single-lead ECG, substantial increases in adenoma detection rate and melanoma triage accuracy, automated pancreatic cancer detection on routine CT with 89–90% sensitivity, and significant improvements in palliative care consultation rates and post-PCI outcomes using AI-supported telemedicine. Over 850 FDA-cleared AI devices exist as of November 2025, with cardiology and radiology dominating clinical adoption. Conclusions: AI has transitioned from experimental to clinically indispensable in multiple specialties, delivering measurable reductions in mortality, morbidity, hospitalizations, and healthcare resource utilization. Remaining challenges include external validation gaps, bias mitigation, and the need for large-scale prospective trials before universal implementation.

The aim of this study is to analyze the psychogenic and clinical factors influencing the emotional state of women during pregnancy and to discuss the possibilities for the treatment and prediction of mental disorders using artificial intelligence (AI). The article presents the physiological and psychosocial determinants of emotional functioning in pregnant women, with particular emphasis on risk factors for mood disorders, such as depression and anxiety. Contemporary therapeutic approaches utilized in the perinatal period are discussed, encompassing both medical methods and non-medical forms of psychological support. Special attention is devoted to the role of modern technologies—including machine learning algorithms, mobile applications, and chatbots—in the process of early disorder detection and mental health support for pregnant women. Furthermore, the significance of diagnostic tools, such as ultrasonography (USG) and cardiotocography (CTG), in monitoring fetal well-being is addressed, alongside their potential integration with AI-based systems. The literature review indicates that AI can serve as a significant supplement to traditional medical care, contributing to improved diagnostic quality, enhanced predictive capabilities and increased accessibility of support for pregnant women.

Background: Contemporary intensive care units (ICUs) generate vast amounts of real-time data, leading to staff cognitive overload and the phenomenon of alarm fatigue. Traditional prognostic scoring systems (APACHE II, SOFA) are inherently static and fail to account for dynamic clinical trends. Artificial intelligence (AI) algorithms, including machine learning (ML) and deep learning (DL), offer novel capabilities for continuous monitoring and early prediction of life-threatening conditions. Objective: To analyze the clinical potential and implementation challenges associated with the deployment of AI algorithms in intensive care settings. Methods: A comprehensive literature review was conducted across the PubMed, Embase, and IEEE Xplore databases for the years 2018–2026, focusing on studies utilizing multicenter databases (such as MIMIC-IV and eICU). Results: AI algorithms demonstrate high discriminative performance (AUROC 0.79–0.96) in the early detection of sepsis, prediction of hemodynamic instability with a 5-to-15-minute lead time, and dynamic mortality risk estimation. In the context of mechanical ventilation, deep learning models (specifically convolutional neural networks, CNNs) enable automated detection of patient-ventilator asynchrony with an accuracy exceeding 90%, as well as precise forecasting of extubation success. Conclusions: Artificial intelligence holds immense potential for optimizing ICU patient care. However, primary implementation barriers persist, including the lack of model interpretability (the "black box" problem), domain shift, and ethico-psychological dilemmas, such as clinician moral distress and the risk of care dehumanization. AI systems should function strictly as clinical decision support tools under physician supervision.

Obesity represents one of the most significant public health challenges worldwide, serving as a key risk factor for numerous chronic diseases, including type 2 diabetes and cardiovascular diseases. The aim of this article was to present modern approaches to obesity management, with particular emphasis on the role of health behaviors, diet therapy, dietary catering services, and semaglutide-based pharmacotherapy. This paper reviews issues related to the concept of health and health behaviors, principles of proper nutrition, and selected dietary models used in obesity treatment. Special attention was paid to dietary catering as a modern tool supporting the implementation of nutritional recommendations, as well as pharmacotherapy utilizing GLP-1 receptor agonists. The conducted analysis indicates that effective obesity treatment requires a comprehensive and interdisciplinary approach, combining lifestyle modification, appropriately tailored diet therapy, and pharmacological support. Modern solutions, such as personalized nutrition and the use of semaglutide-based medications, can significantly increase treatment efficacy and promote long-term weight reduction.

Introduction and objective: The study examines the vaccination status and attitudes of Ukrainian refugees in Poland, focusing on factors influencing decisions regarding mandatory vaccinations. Given the ongoing migration and geopolitical instability, understanding these determinants is crucial...