Background: Artificial intelligence (AI) has made significant advances in recent years, and its applications in psychiatry have gained increasing attention. The use of AI in psychiatry offers the potential to improve patient outcomes and provide valuable insights for healthcare workers. However, the potential benefits of AI in psychiatry are accompanied by several challenges and ethical implications that require consideration. In this review, we explore the use of AI in psychiatry and its applications in monitoring mental illness, treatment, prediction, diagnosis, and deep learning. We discuss the potential benefits of AI in terms of improved patient outcomes, efficiency, and cost-effectiveness. However, we also address the challenges and ethical implications associated with the use of AI in psychiatry, including issues of accuracy, privacy, and the risk of perpetuating existing biases in the field. Results: This is a review article, thus not applicable. Conclusion: Despite the challenges and ethical implications of using AI in psychiatry, the potential benefits of this technology cannot be ignored. Further research and development are required to address the limitations and ensure the safe and ethical integration of AI in the field of psychiatry. By doing so, AI has the potential to greatly improve patient outcomes and enhance the delivery of mental healthcare services.

Background: Mental health literacy (MHL) and help-seeking behaviors are pivotal in managing mental well-being, especially among Egyptian undergraduates. Despite the importance and prevalent psychological distress in this group, limited research has addressed MHL and associated behaviors in Egypt. This study aimed to assess the levels of MHL and help-seeking behavior among Egyptian university students. Methods: A cross-sectional study was conducted across ten Egyptian universities during the academic year 2022–2023. A convenience sample of 1740 students was obtained through online questionnaires distributed via social media platforms. The survey comprised demographic characteristics, the Mental Health Literacy Scale (MHLS), and the General Help Seeking Behavior Questionnaire (GHSPQ). Results: Among 1740 Egyptian undergraduates, medical students scored higher in recognizing disorders (p < 0.05), while non-medical students excelled in attitudes (p < 0.05). A strong correlation was observed between attitudes toward mental illness and total mental health literacy (coefficients of 0.664 and 0.657). Univariate analysis indicated a significant association with professional help-seeking (OR = 1.023). Females, individuals aged 21 or above, and non-medical students were more likely to seek mental health information (OR = 1.42, 1.82, 1.55 respectively). Help-seeking behavior for emotional problems was more inclined towards intimate partners, whereas suicidal thoughts prompted seeking professional help. Conclusion: The findings advocate for comprehensive mental health education, particularly in rural areas, and emphasis on the role of personal relationships in mental well-being. Implementing these insights could foster improved mental health outcomes and reduce related stigma in Egypt.

Background: Mental health disorders are a major public health concern especially among undergraduates, globally and within Egypt. Most individuals suffering from mental illnesses either do not seek care at all or seek it only after a large delay. It is therefore critical to identify the barriers that prevent them from seeking professional help to solve the problem from its roots. Thus, the objectives of the study were to assess the prevalence of psychological distress, the need for professional mental health care, and the barriers to seeking available services among undergraduate students in Egypt. Methods: A proportionate allocation technique was used to recruit 3240 undergraduates from 21 universities. Symptoms of psychological distress were evaluated using the Arabic General Health Questionnaire (AGHQ-28) and a score of above nine was used to identify positive cases. The pattern for utilization of mental health care was assessed using a multi-choice question and barriers to mental health care were assessed using the Barriers to Access to Care Evaluation (BACE- 30) tool. Logistic regression was adopted to identify the predictors of psychological distress and seeking professional health care. Results: The prevalence of psychological distress was 64.7% and the need for professional mental health care was 90.3% of those with psychological distress. The top barrier to receiving professional mental health services was wanting to solve the problem on their own. Logistic regression revealed that female sex, living away from family and positive family history of mental disorders were independent predictors of psychological distress. Students from urban backgrounds were more likely to seek help than students from rural ones. While age above 20 and positive family history of mental disorders were independent predictors for seeking professional help. There is no significant difference between medical and non-medical students in terms of psychological distress. Conclusion: The findings of the study showed there is a high prevalence of psychological distress and a lot of instrumental and attitudinal related barriers to seeking mental health care and highlighted the urgent need to develop interventions and preventive strategies to address the mental health of university students.

Background: Gastroesophageal reflux disease (GERD) is a common digestive problem in adults particularly medical students, who are one of the most vulnerable groups. Many variables, including lifestyle changes and psychological stress, increase the prevalence of GERD among undergraduate medical students. Therefore, this study aims to assess the prevalence, and risk factors of GERD, and its relationship with perceived stress among medical students in Egypt. Methods: In November and December of 2022, a cross-sectional descriptive study with an analytical component was carried out among medical students from six different universities. An online self-reported questionnaire was used to collect data. The questionnaire included data on sociodemographic characteristics, risk factors, lifestyle, the Arabic version of GerdQ, and the Arabic version of Cohen’s Perceived Stress Scale (PSS). Results: The questionnaire was filled out by 964 medical students, the majority of whom were female (64%). Overall, 17.1% of participants reported symptoms of GERD. Logistic regression showed that smoking, high perceived stress, and a family history of GERD were the independent predictors of having GERD symptoms, with odds ratios of 4.1, 3.9, and 2.2, respectively. Conclusion: GERD is a frequent condition among Egyptian medical students, affecting around one-fifth of them. In the fight against GERD, university-based smoking cessation and stress management programs may be influential.

Introduction: Artificial intelligence (AI) is revolutionizing healthcare, particularly in disease diagnostics, by improving accuracy, efficiency, and personalization. Its applications span medical imaging, pathology, and personalized medicine, significantly enhancing patient outcomes. However, challenges such as ethical dilemmas, data privacy concerns, and algorithmic biases hinder its full integration into clinical practice. A critical gap in the literature is the lack of comprehensive frameworks for addressing these challenges, particularly in low-resource settings. Aim: We aim to explore the current advancements, applications, and challenges of AI in disease diagnostics, emphasizing its transformative impact on healthcare systems. Materials and methods: A narrative review was conducted to explore the role of AI in disease diagnostics and healthcare. Results: AI has shown remarkable success in various domains such as medical imaging, pathology, and personalized medicine. Key technologies include machine learning, deep learning, and natural language processing, which have improved diagnostic accuracy and efficiency. Applications such as cancer detection, drug development, and wearable health monitoring devices have demonstrated a significant impact. However, challenges persist, including ethical dilemmas, algorithmic bias, regulatory gaps, and data security concerns. Innovative solutions like interdisciplinary collaboration, synthetic data generation, and robust legal frameworks are recommended to address these issues. Conclusion: AI’s integration into disease diagnostics has the potential to revolutionize healthcare by improving outcomes and efficiency. Nonetheless, overcoming ethical, technical, and societal challenges is critical for realizing its full potential. Continued advancements in AI, combined with responsible implementation, can transform healthcare systems and pave the way for more equitable and effective medical practices.

Surgical site infections (SSIs) are among the most common postoperative complications following cesarean section, particularly in Africa. These infections pose maternal health risks, including prolonged hospitalization, increased healthcare costs, and mortality. This systematic review and meta-analysis aimed to evaluate the epidemiology, pooled prevalence, and risk factors for SSIs after cesarean section in Africa. A systematic search of PubMed/MEDLINE, Scopus, and Web of Science databases was conducted to identify studies published between January 2000 and December 2023. The review followed PRISMA 2020 guidelines, and 41 studies spanning 18 African countries met the inclusion criteria. Data on SSI prevalence and risk factors were extracted, and the quality of studies was assessed using the Newcastle–Ottawa Scale. A random-effects model was used to estimate pooled prevalence, with subgroup analysis, sensitivity analyses, and meta-regression exploring variations across study characteristics. Publication bias was assessed using funnel plots. The pooled prevalence of SSIs after cesarean section was 11% (95% CI: 9–12.9%) with substantial heterogeneity (I2 = 97%, < 0.001). Regional variations were observed, with the highest prevalence in Tanzania (34.1%) and Uganda (15%), and the lowest in Tunisia (5%) and Egypt (5.3%). Temporal trends revealed a peak in prevalence (16%) during 2011–2015, declining to 9.8% by 2016–2020. Prolonged rupture of membranes (PROM) was the most frequently reported risk factor (OR: 4.45–13.9), followed by prolonged labor (> 24 h) (OR: 3.48–16.17) and chorioamnionitis (OR: 4.37–9.74). Potential publication bias indicated by asymmetrical funnel plots. SSIs following cesarean section remain a burden in Africa, with wide regional variations and multiple preventable risk factors. The findings highlight the need for targeted interventions, including improved infection control practices, antenatal care, and timely management of obstetric complications.