Medical education is a lifetime learning process stretching from undergraduate to postgraduate, specialty training, and beyond. It also applies to various healthcare professionals, including doctors, nurses, and other allied healthcare professionals. Therefore, it is essential to acknowledge the immense role of artificial intelligence in medical education in the current era of rapidly growing technology. High-quality data that met the study objectives were included. In addition, comprehensive investigations on articles available in reputable databases such as PubMed, Research Gate, PubMed central, Web of Science, and Google Scholar were considered for literature review. Artificial intelligence has fixed various issues in education during the last decade, including language processing, reasoning, planning, and cognitive modelling. It can be used in medical education in the following forms: Virtual Inquiry System, Medical Distance Learning and Management, and Recording teaching videos in medical schools. It can also enhance the value of the non-analytical humanistic aspects of medicine. The goal of this review article was to present the implications of AI in medical education, now and in the coming years.

Background: Medical students encounter various stressors during their studies. The study aimed to assess stress levels, sources, and associated determinants among Sudanese medical students. Method: An online questionnaire-based study was conducted among 617 undergraduate medical students of 6 different universities in Sudan. A 19-item questionnaire was utilized to assess stress sources, in addition to demographics, stress manifestations, and coping strategies. Results: The overall prevalence of stress was 31.7% (p < 0.05). The main sources of stress were time pressure, heavy workload, fear of failure, and examination frequency. One-third of students indicated that they experienced at least one source of psychosocial- and teaching-related stress. Female medical students were more significantly stressed due to academics than males. Also, fourth- and fifth-year students were academically more stressed in comparison to the first-year students. Poisson regression analysis model showed that first-year students were less stressed than the final-year students in relation to academics (odds = 0.888, P = 0.003). Male medical students, across all study years, were far less stressed than females (odds = 0.901, P = 0.000153). Expectedly, ‘studying medicine by choice’ was associated with decreased odds for psychosocial stressors (odds = 0.885, P = 0.00781), and improved model-fit (chi-squared = 6.8952, P = 0.008643). Also, the year of study was a predictor of teaching-related stress development. Conclusions: Female medical students were more stressed due to academics than males. On the other hand, final-year students were more academically stressed than first-year students. Female medical students were likely having stress related to academic stress development, while being first-year medical student was a predictor of not developing academic stress. Studying medicine by choice’ was associated with ability to cope against stress.

Objective: The severity and chronicity of PMS can lead to the impairment of studies, and it can also affect relationships, activities, quality of life (QoL), and academic performance. This study aimed to determine PMS frequency and its associated factors in order to assess the quality of life (QoL) among female medical students at Bisha University, Saudi Arabia. Methods: This study was cross-sectional and included 388 female medical students in the Faculty of Medical Applied Sciences and the Faculty of Medicine at Bisha University. The participants all filled in a self-administered questionnaire. The Premenstrual Syndrome Scale (PSS) was used based on the diagnostic and statistical criteria for PMS assessment. PMS was diagnosed after the presence of five or more severe premenstrual symptoms had been resolved following menstruation (adapted from American Psychiatric Association). Data Analysis: The data obtained were analyzed using SPSS 25.0. A chi-square test was used to test the associations between the study variables. A logistic regression analysis technique was used to select the group of variables. Participants were asked to provide consent to participate in the study. IRB was obtained from the University of Bisha, College of Medicine. Results: The participants were aged 19.5 ± 4.9 years, and the prevalence of PMS was 64.9%. Most of the female students were of extroverted personality types (35%). In addition, 13.4% were obese or overweight, and 19.5% of the 50% with PMS exercised regularly (p < 0.05). Menstruation significantly influenced the related quality of life subscales (p < 0.05). Conclusion: PMS significantly influenced daily activities related to quality of life and homework. Moreover, almost half of the female students experienced the effects of menstruation in their learning environment. Therefore, among female students, the modification of risk factors should be considered a critical intervention point.

Purpose: Since the emergence of the COVI9-19 pandemic, there has been concern about the availability and sufficiency of blood transfusions to meet patient needs. Also, the adequate and safe blood supply is a major issue to fulfill present demands. This study aims to determine the impact of the COVID-19 pandemic on the blood transfusion service and identify solutions to overcome this impact from the perspective of blood donors and healthcare professionals. Methods: This study was conducted prospectively among Saudi Arabian blood donors and healthcare professionals between May 2020 and May 2021. A group of professional health care professionals and blood donors participated in this study to determine the impact of the COVID-19 pandemic on blood transfusion services and identify possible solutions to overcome the adverse effects on blood system activities. Results: To understand the impact of pandemic COVID-19 on blood transfusion services in Saudi Arabia, we investigated population dynamics and statistical analysis from different age groups and health care professionals. A total of 424 blood donors and 372 healthcare professionals participated in this study. The suggested solution by the participants to overcome the blood shortage during pandemics was the organization of mobile blood drives at donors’ homes, work, and educational sites. Statically significant associations were found between blood donation during this pandemic and age (p=0.019) and marital status (p=0.001). Conclusion: The findings of this study highlight the main problems of blood transfusion due to the onset of COVID-19. Hence, the data could be a source of improving blood transfusion and blood donation systems by healthcare centers and on an individual basis. Healthcare professionals use this public management system in hospitals to minimize the adverse effects of the pandemic.

Objectives: Ongoing military conflict in Sudan has had significant repercussions on the health and well-being of the population, particularly among women of reproductive age. This study aimed to investigate the impact of conflict on maternal health by employing a mixed qualitative and quantitative research approach. Methods: Through in-depth interviews and survey questionnaires (388 women), this study examined the experiences and challenges faced by pregnant women and new mothers and the availability and accessibility of maternal healthcare services in conflict-affected areas. Using a qualitative approach, in-depth interviews were conducted with 35 women who had recently given birth or were pregnant in regions affected by the Khartoum State–Sudan conflict. Thematic analysis was used to analyze the data collected from the interviews. Result: Most women did not have access to healthcare services (86.6%), and out of the total sample, 93 (24%) experienced adverse outcomes. The factors associated with adverse effects were parity (OR 1.78, CI [1.15–2.75], p-value 0.010), gestational age (OR 2.10, CI [1.36–3.25], p-value 0.002), access to healthcare (OR 2.35, CI [1.48–3.72], p-value 0.001), and delivery mode (OR 1.68, CI [1.05–2.69], p = 0.030). Factors significantly associated with accessibility to maternal healthcare services included age (OR, 1.28; = 0.042) and higher conflict levels (1.52 times higher odds, p = 0.021). The narratives and experiences shared by women exposed the multifaceted ways in which the conflict-affected maternal health outcomes. Conclusion: The significance of this study lies in its potential to contribute to the existing literature on maternal health in conflict-affected areas, especially in Sudan, and to help us understand how women can receive maternal health services.

Introduction: With the incorporation of artificial intelligence (AI), significant advancements have occurred in the field of fetal medicine, holding the potential to transform prenatal care and diagnostics, promising to revolutionize prenatal care and diagnostics. This scoping review aims to explore the recent updates in the prospective application of AI in fetal medicine, evaluating its current uses, potential benefits, and limitations. Methods: Compiling literature concerning the utilization of AI in fetal medicine does not appear to modify the subject or provide an exhaustive exploration of electronic databases. Relevant studies, reviews, and articles published in recent years were incorporated to ensure up-to-date data. The selected works were analyzed for common themes, AI methodologies applied, and the scope of AI’s integration into fetal medicine practice. Results: The review identified several key areas where AI applications are making strides in fetal medicine, including prenatal screening, diagnosis of congenital anomalies, and predicting pregnancy complications. AI-driven algorithms have been developed to analyze complex fetal ultrasound data, enhancing image quality and interpretative accuracy. The integration of AI in fetal monitoring has also been explored, with systems designed to identify patterns indicative of fetal distress. Despite these advancements, challenges related to the ethical use of AI, data privacy, and the need for extensive validation of AI tools in diverse populations were noted. Conclusion: The potential benefits of AI in fetal medicine are immense, offering a brighter future for our field. AI equips us with tools for enhanced diagnosis, monitoring, and prognostic capabilities, promising to revolutionize the way we approach prenatal care and diagnostics. This optimistic outlook underscores the need for further research and interdisciplinary partnerships to fully leverage AI’s potential in driving forward the practice of fetal medicine.