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.

Introduction: The basic postulate of the lean concept is that greater efficiency of the work process can be achieved through a process of continuous improvement,which aims to eliminate waste and maximize activities that add value.The Netherlands, Great Britain, Italy and the United States were examples of healthcare systems that implemented the lean concept.Aim: To examine the opinion of health workers of a public hospital in the Sarajevo area about the possibilities of improving business processes by implementing thelean concept.Method: The cross-sectional study was conducted using a questionnaire. The questionnaire was sent to the e-mail addresses of health workers of public health organizations. 91 respondents of both sexes (doctors ofmedicine, residents, registered nurses, nurses and administrative staff) were included.Results: A positive correlation was establishedin the attitude toward the effectiveness and efficiency of business processes. According to the participant’s opinion, the greater effectiveness of business processes contributes to greater efficiency (r=0.846; p<0,05). Spearman’s coefficient rs=0.81 shows a strong connection between the effectiveness and efficiency of business processes.Conclusion: The study showed that there is a positive attitude towards the impact of the lean concept on improving the efficiency of business processes. The reducexpected positive effects of the implementation of the lean concept are manifested through faster provision of services to patients, reduction of service waiting times and general improvement of business processes.

BACKGROUND: Heat-not-burn (HNB) technology by the U.S. Food and Drug Administration has been classified as a modified risk tobacco product, which can be a better option for those populations who cannot give up the habit of smoking. The outlook on the effects of these products is quite controversial in the scientific world. OBJECTIVE: To present the effect of HNB tobacco products on the cardiovascular system, with reference to the existence of possible benefits of the technology. METHODS: The literature search was conducted in PubMed/Medline, the Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov databases, with reliance on a well-defined guiding research statement. Quality appraisal was performed using the CASP checklist for randomized controlled trials. RESULTS: The search of three databases identified 167 records, and after selection process, 25 randomized controlled trials were eligible for our study’s criteria. Twenty studies investigated the effects of HNB products on biomarkers of clinical relevance. Five studies evaluated other functional heart parameters rather than biomarkers. CONCLUSION: With HNB tobacco products, significant reductions were found in biomarkers of exposure and biological effect related to pathways involved in cardiovascular disease, including inflammation, oxidative stress, lipid metabolism, platelet function, and endothelial dysfunction.