Objectives: Artificial intelligence (AI) holds the promise to revolutionize the field of medicine and enhance the well-being of countless patients. Its capabilities span various areas, including disease prevention, accurate diagnosis, and the development of innovative treatments. Moreover, AI has the potential to streamline health-care delivery and lower expenses. The community should be aware of the potential applications of AI in health care, so that they can advocate for its development and adoption. Hence, the objective of this study is to assess the community’s perspectives regarding the utilization of AI in health care. Methods: A cross-sectional, questionnaire-based study was conducted in Saudi Arabia during the period of June to October 2023. The questionnaire was distributed to people on various social media platforms using a convenience sampling method. The collected data were analyzed using Statistical Package for the Social Sciences. Results: The study included 771 individuals, with 42.5% having a positive outlook on the use of AI in health care, 31.8% having a neutral view, and 7.5% having a negative view. The only factor associated with a positive opinion was regional differences ( P = 0.006). Moreover, participants who used medical apps or programs ( P = 0.026), wearables ( P = 0.027), felt more confident in using technology ( P < 0.001), enjoyed using technology ( P < 0.001), found it easier to familiarize themselves with new devices or programs ( P < 0.001), and had more knowledge about AI ( P < 0.001) had more positive opinions regarding the use of AI in health care. Conclusions: The study found that most Saudis, especially those who were familiar with the use of technology, support the use of AI in health care, with a positive or neutral view. Yet, targeted campaigns in certain regions are needed to educate the entire community about AI’s potential benefits.

Purpose: Alopecia Areata (AA) is a complex autoimmune condition characterized by long-term inflammatory non-scarring patches of hair loss on the face, scalp, and body. Its development involves a combination of genetic, immunological, and environmental factors, making it challenging to understand and treat. This study aims to assess the awareness, beliefs, and psychological impact of patients Patients and Methods: This cross-sectional study was conducted in Eastern and Central provinces of Saudi Arabia and utilized online questionnaires to evaluate knowledge, beliefs, and psychological effects of patients with Alopecia Areata. These questionnaires were distributed using social media platforms. Results: This study enrolled 248 patients with Alopecia Areata (AA), of whom 38.7% were aged 31-40 years and 55.2% were males. The majority were Saudis (98.4%), married (54%), and residing in the Central Region (60.1%). Nearly half (48.8%) were employed in non-medical fields, and 30.6% were vocational school graduates. Healthcare providers were the main source of AA information (52.8%), with most patients aware of AA's seriousness, curability, and quality of life impact, though many had low knowledge and experienced negative psychological effects such as shyness, anxiety, and depression. Conclusion: Despite low self-rated knowledge, most patients recognized AA as serious and requiring treatment. Psychological wellbeing was heavily impacted by AA severity, quality of life, disease duration, and treatment efficacy. Although many patients showed improved AA status and good medication compliance, adverse psychological effects persisted, highlighting the need for larger studies to better understand and address these issues.

Background: Induction of labor is the process of artificially stimulating the uterus to start labor before the spontaneous onset of labor. It has several medical indications. Commonly used agents are vaginal misoprostol, vaginal prostaglandin E2 (dinoprostone), and oral misoprostol. Methods: Through October 2023, a literature review was carried out in Cochrane, PubMed, Web of Science, and Scopus to identify randomized clinical studies assessing if oral and vaginal misoprostol has better efficacy of induction of labor over vaginal prostaglandin E2 or dinoprostone as a primary outcome. The data were pooled as mean difference, risk ratio, and 95% confidence interval. Results: Fifty-three RCTs involving 10,455 patients showed a statistically significant difference in the overall success rate of induction between the misoprostol and prostaglandins E2 (PGE2) groups. They required less additional oxytocin compared to the PGE2 groups. The frequency of tachysystole, uterine hyperstimulation, abnormal cardiotocography, meconium-stained amniotic fluid, and Apgar score <7 at 1 minute were all higher in misoprostol groups than in PGE2 groups. No difference was found in cesarean section, fever, Neonatal Intensive Care Unit admission, or Apgar scores at 1 minute or 5 minutes. Conclusion: Vaginal misoprostol is more effective at inducing labor but may be less safe than vaginal dinoprostone. Oral misoprostol is generally as safe as vaginal dinoprostone. Vaginal dinoprostone requires lower doses but may need more oxytocin administration.

Background and Objectives: On a local and national scale, genitourinary malformations (GUMs) are the second most encountered congenital anomaly in children. GUMs are linked to several risk factors, including maternal co-morbidities and insufficient folic acid. They may also be related to maternal health and care during pregnancy. Expanding our knowledge about these factors is necessary for the development of preventative measures, which could reduce GUM incidence. This study evaluated the local youth’s understanding and perceptions of genitourinary anomalies. Materials and Methods: This cross-sectional, qualitative, anonymous, questionnaire-based study involved members of the local population, aged 18 years or over. Based on a 5% type I error rate (α = 0.05) and an 80% response rate, a sample size of 481 was determined. The questionnaire was completed by 902 people. The data were analyzed using SPSS version 25 (IBM). Results: Over half (57%) of respondents believed that hormonal therapy during pregnancy could increase GUM risk. Moreover, 46% thought that maternal chronic diseases could be another risk factor, while 43% believed that pregnancy-related conditions, such as pre-eclampsia, increased GUM risk. Women had higher odds of high perception scores than men, according to the univariate and multivariate analyses. Most participants (74%) strongly agreed that proper and ongoing prenatal follow-ups are necessary, 69% agreed that premarital medical check-ups are necessary, and 67% believed that optimal nutrition throughout pregnancy is necessary to reduce GUM risk. Conclusions: The results emphasize the necessity of developing healthcare strategies specifically designed to increase knowledge about GUMs and overcome incorrect community perceptions of risk factors that could also help improve attitudes towards prevention and ultimately reduce the incidence of GUMs.

Trauma-induced coagulopathy (TIC) at admission to the emergency department is associated with severe bleeding, massive transfusion, increased morbidity, and mortality. There are significant regional variations in the accessibility and acceptance of point-of-care technology, such as viscoelastic hemostatic testing (VET), for coagulation monitoring and bleeding management in the Arabian Gulf council countries (GCC). Notable regional differences among these countries are mainly due to variation in the awareness of the benefits of VET-guided bleeding management using rotational thromboelastometry (ROTEM) or thromboelastography (TEG), compared with a transfusion ratio-based approach using shock packs, rather than for economic reasons. This review highlights the superiority of VET-guided bleeding management in severe trauma over conventional coagulation tests (CCTs) for early TIC detection and the importance of monitoring hemostatic disorders, whether hypocoagulability or hypercoagulability. VET consistently outperforms traditional coagulation tests in detecting early coagulation abnormalities and predicting massive transfusion and mortality in major trauma. Besides hyperfibrinolysis, fibrinogen deficiency often represents the first issue in TIC. Recent meta-analyses consistently demonstrated a reduction in mortality by implementing VET-guided bleeding management algorithms in severe trauma. Several studies demonstrated that VETs are valuable tools for massive transfusion and mortality prediction in bleeding patients with severe trauma. There is a notable variation in VET adoption and practice across trauma settings in the Arab Gulf. GCC-specific primary evidence is limited, with the Qatar cohort representing the largest directly identified regional study.

Background/Objectives: Glaucoma is a leading cause of irreversible blindness globally and must be detected early to preserve vision. Standard methods, such as automated perimetry and optical coherence tomography (OCT), are limited in their ability to detect early changes. Therefore, this systematic review evaluates the diagnostic and predictive performance of artificial intelligence (AI)-driven OCT-based models, including those based on deep learning, machine learning, and transformer architectures, and contextualizes their performance against the recognized limitations of standard automated perimetry and conventional OCT trend analysis. Methods: The PubMed, Ovid, and Google Scholar databases were comprehensively searched to identify relevant published research in English. Screening of observational, cohort, prospective, retrospective and diagnostic accuracy studies, as well as clinical trials, identified 13 studies. Their key outcomes, including sensitivity, specificity, and area under the curve (AUC), were extracted for synthesis. Results: AI-driven OCT-based models generally reported high diagnostic performance, with mean sensitivities and specificities exceeding 80%, as well as AUC values generally above 0.85. AI-driven OCT-based models were reported to detect glaucoma progression as early as 9 months before standard methods. Conclusions: The findings suggest that AI-driven OCT-based models may improve glaucoma detection by identifying subtle structural and functional changes earlier, which may help inform earlier intervention decisions. However, the majority of included studies relied on internal validation only, and prospective multicenter studies with external validation are required before AI-driven OCT-based models can be reliably implemented in routine glaucoma care.