Menopause represents an inevitable transition in a woman’s life, presenting with vasomotor symptoms, mood disorders, sleep difficulties, and prolonged risks such as osteoporosis and cardiovascular diseases. Hormone replacement therapy emerged as the cornerstone of menopausal management, particularly for alleviating symptoms and preventing postmenopausal osteoporosis. However, findings from the Women’s Health Initiative (WHI) study in 2002 highlighted increased risks of breast cancer, cardiovascular disease, and stroke associated with hormonal replacement treatment, leading to a significant global decline in its usage. Consequently, numerous women were deprived of essential therapy, endangering their health and quality of life. This review presents the findings of the WHI study, discusses its methodological errors, and evaluates its benefits and harms. We explore landmark studies that have reestablished the benefits and risks of hormone replacement therapy over the past two decades. Guidelines supported by these findings are presented in this review. Despite advancements, public perception of hormone replacement treatment remains influenced by outdated findings, limiting its utilization in many regions, especially in developing countries. Our objective is to provide evidence that misconceptions about hormone replacement therapy significantly impact women’s general health and quality of life, as well as to clarify the short-term and long-term impacts of hormone replacement therapy. We conclude that hormonal replacement treatment is effective and safe when administered according to established guidelines. Access to information, coupled with knowledgeable physicians who consistently interact with women, is as vital as the contributions of menopause healthcare specialists. Conflicting information from outdated professionals can likely lead to treatment failure in patients. Keywords: menopause, women’s health, estrogens, progestins, quality of life

This paper’s primary aim is to examine the impact of managerialcompetencies on the performance of healthcareorganizations in Bosnia and Herzegovina, with a particularfocus on the role of middle management.Research Methodology: A quantitative research approachwas employed, and data were collected through a structuredquestionnaire designed to measure six key dimensionsof managerial competencies: leadership, strategicthinking, communication, decision-making, teamwork, andchange management. The construction of the questionnairewas based on previous relevant research and theoretical models of managerial competencies, with particularattention given to the models developed by Boyatzis (1982)and later expanded by Whetten and Cameron (2011), as wellas findings from research on healthcare management, suchas Calhoun et al. (2008) on competencies for healthcareleaders. The items were adapted to the specific context ofhealthcare institutions in Bosnia and Herzegovina, and eachitem was rated on a 5-point Likert scale (1- Strongly Disagree;2-Disagree; 3 – Neutral; 4- Agree; 5- Strongly Agree).The questionnaire was distributed to a purposive sample of120 middle managers working in various healthcare institutionsacross Bosnia and Herzegovina. Data were collectedduring three months from January to April 2025. Descriptivestatistics were used for data analysis.Conclusion: The results indicate that communication andteamwork competencies were rated most positively and significantlycorrelated with organizational outcomes. In contrast,strategic thinking and change management receivedlower ratings. The instrument’s reliability was confirmedthrough high internal consistency (Cronbach α > 0.70).

The paper explores the evolving role of Artificial Intelligence (AI) in perinatal medicine and hu-man reproduction, highlighting its potential to transform clinical practices. AI technologies are being utilized to improve diagnostic accuracy, personalize treatment, and enhance patient care, particularly in areas like perinatal ultrasound, fetal heart rate monitoring, and fetal neurology. The Kurjak Antenatal Neurodevelopmental Test (KANET) exemplifies how AI can aid early detection of neurodevelopmental disorders. However, the integration of AI presents challenges such as data quality concerns, algorithmic bias, ethical concerns, and the need for robust regulatory frameworks. The authors argue that while AI offers significant opportunities, its implementation must be carefully managed to avoid over-reliance on technology and ensure equitable healthcare access. The paper concludes that the current state of AI in this field marks not an endpoint but a critical phase of growth and development, necessitating a balanced approach that combines innovation with ethical and practical considerations.

Abstract Objectives Prenatal cardiology is a part of preventive cardiology based on fetal echocardiography and fetal interventional cardiology, which facilitates treatment of congenital heart defects (CHD) in pediatric patients and consequently in adults. Timely prenatal detection of CHD plays a pivotal role in facilitating the appropriate referral of pregnant women to facilities equipped to provide thorough perinatal care within the framework of a well-structured healthcare system. The aim of this paper is to highlight the role of left atrial strain (LAS) in prenatal evaluation of fetal heart and prediction of structural and functional disorders. Methods We conducted a comprehensive literature review searching PubMed for articles published from inception up until August 2023, including the search terms “left atrial strain”, “fetal echocardiography”, and “prenatal cardiology” combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. Results Our review underscores the significance of LAS parameters in fetal echocardiography as a screening tool during specific gestational windows (starting from 11 to 14 weeks of gestation, followed by better visualization between 18 and 22 weeks of gestation). The left atrial strain technique and its parameters serve as valuable indicators, not only for identifying cardiac complications but also for predicting and guiding therapeutic interventions in cases of both cardiac and noncardiac pregnancy complications in fetuses. Evidence suggests establishment of second-trimester reference strain and strain rate values by speckle-tracking echocardiography in the healthy fetal cohort is essential for the evaluation of myocardial pathologies during pregnancy. Conclusions Finding of LAS of fetal heart is feasible and probably can have potential for clinical and prognostic implications.

BACKGROUND: Following the latest trends in the development of artificial intelligence (AI), the possibility of processing an immense amount of data has created a breakthrough in the medical field. Practitioners can now utilize AI tools to advance diagnostic protocols and improve patient care. OBJECTIVE: The aim of this article is to present the importance and modalities of AI in maternal-fetal medicine and obstetrics and its usefulness in daily clinical work and decision-making process. METHODS: A comprehensive literature review was performed by searching PubMed for articles published from inception up until August 2023, including the search terms “artificial intelligence in obstetrics”, “maternal-fetal medicine”, and “machine learning” combined through Boolean operators. In addition, references lists of identified articles were further reviewed for inclusion. RESULTS: According to recent research, AI has demonstrated remarkable potential in improving the accuracy and timeliness of diagnoses in maternal-fetal medicine and obstetrics, e.g., advancing perinatal ultrasound technique, monitoring fetal heart rate during labor, or predicting mode of delivery. The combination of AI and obstetric ultrasound can help optimize fetal ultrasound assessment by reducing examination time and improving diagnostic accuracy while reducing physician workload. CONCLUSION: The integration of AI in maternal-fetal medicine and obstetrics has the potential to significantly improve patient outcomes, enhance healthcare efficiency, and individualized care plans. As technology evolves, AI algorithms are likely to become even more sophisticated. However, the successful implementation of AI in maternal-fetal medicine and obstetrics needs to address challenges related to interpretability and reliability.