This review aims to analyze the development and impact of Artificial Intelligence (AI) in the context of Saudi Arabia’s public healthcare system to fulfill Vision 2030 objectives. It is extensively devoted to AI technology ... | Find, read and cite all the research you need on Tech Science Press

In recent years, Artificial Intelligence (AI) is transforming healthcare systems globally and improved the efficiency of its delivery. Countries like Saudi Arabia are facing unique adoption challenges in their public healthcare, these challenges are specific to AI literacy, understanding and effective usage of AI technologies. In addition, cultural, regulatory and operational barriers increase the complication of integrating AI literacy into public healthcare operations. In spite of its critical contribution in enabling sustainable healthcare development, limited studies have addressed these adoption challenges. Our study explores the AI literacy adoption barriers in context to Saudi Arabian public healthcare sector, focusing on its relevance for advancing healthcare operations and achieving Sustainable Development Goals (SDGs). The research aims to identifying and addressing the adoption challenges of Artificial Intelligence literacy within the public healthcare in Saudi Arabia. The research aims to enhance the understanding of AI literacy, its necessity for enhancing healthcare operations, and the specific hurdles that impede its successful AI adoption in Saudi Arabia’s public healthcare ecosystem. The research employs a qualitative analysis using the T-O-E framework to explore the adoption challenges of AI literacy. Additionally, the Best-Worse Method (BWM) is applied to evaluate the adoption challenges to AI literacy adoption across various operational levels within Saudi Arabia’s public healthcare supply chain. The study uncovers substantial adoption challenges at operational, tactical, and strategic level, including institutional readiness, data privacy, and compliance with regulatory frameworks. These challenges complicate the adoption of AI literacy in the Saudi public healthcare supply chains. The research offers critical insights into the various issues affecting the promotion of AI literacy in Saudi Arabia’s public healthcare sector. This evidence-based study provides essential commendations for healthcare professionals and policymakers to effectively address the identified challenges, nurturing an environment beneficial to the integration of AI literacy and advancing the goals of sustainable healthcare development.

The chronic disease burden in Saudi Arabia has created adverse health, social and economic consequences that require urgent attention from health and political authorities. Diabetes has become an epidemic in Saudi Arabia. Data on personal and structural factors associated with diabetes in the Hail region are scarce. Such data are imperative to develop effective strategies to control the epidemic in the region. A cross-sectional study of diabetes patients attending diabetes health care facilities in Hail was conducted using a sample of 392 patients. An interviewer-administered questionnaire was used. A slightly higher proportion of female participants (54.1%) were included in the sample. Most of the participants were from rural areas (73.9%), and 70.9% of the participants were from the middle-age (30–50 years) category. A close proximity to the diabetes clinic (OR = 1.98; 95% CI: 1.08–3.44), good transport facilities (OR = 1.67; 95% CI: 1.11–2.78) and feeling contented with supportive services (OR = 2.03; 95% CI: 1.12–4.04) were associated with patients’ satisfaction with the overall quality of the diabetes clinic services. The presence of good-quality health care professionals working in these treatment centers also seemed to contribute to patients’ satisfaction with the services they received. These structural factors associated with patients’ satisfaction with the services they received from diabetes clinics must be considered in diabetes control programs in the region. The minimization of structural barriers will eventually assist the national strategic plan, Vision 2030, which aims to improve the quality of life of the Saudi people by 2030.

Introduction: In recent years, Artificial Intelligence (AI) is transforming healthcare systems globally and improved the operational efficiency in healthcare organizations. The authors examined how an artificial intelligence (AI)–driven operational management system (OMS) affected operational efficiency in health care units in the Kingdom of Saudi Arabia (KSA). They also investigated the mediating role of staff attitudes in the relationship between OMSs and operational efficiency. This research contributes to the field by applying the theory of planned behavior to examine health care professionals’ perceptions of AI-based OMSs and their impact on operational efficiency.Methods: To achieve study objectives, a quantitative research design, with cross-sectional survey methodology, was used to gather data from 287 health care professionals across various hospitals in the KSA. The authors used a partial least squares structural equation modeling (PLS-SEM) approach to hypothesis testing.Results: The findings indicated that an AI-based OMS significantly improved operational efficiency and positively affected staff attitudes. Furthermore, staff attitudes mediated the relationship between an AI-based OMS and operational efficiency.Discussion: The study finding highlights the dual benefits of AI-based OMSs in enhancing both operational performance and employee satisfaction. The results suggest that health care organizations in the KSA should invest in AI technologies to optimize operational efficiency and improve staff attitudes. The findings also emphasize the need to address employee perceptions to fully capitalize on the benefits of AI implementations. They also introduce staff attitudes as a mediating factor, offering new insights into the interaction between technology and employee engagement.

Aims Knowledge plays a vital role in making better decisions for treatment. Patients should be educated about the knowledge of the disease. This study aimed to know patient knowledge about diabetes illness symptoms, complications, and preventive personal lifestyle factors. Methods A cross sectional research design was used to know diabetes knowledge among 261 diabetes patients. Descriptive analysis and logistic regression were used to analyze collected data. Results A total 261 respondents were included in this study. 71.26 % were male and 28.74% were females, and their mean age was 30.41 ±7.4 years. About 45.2 % of the respondents earned excellent knowledge ratings. The study concluded that frequent urination (94.6%), increased thirst (86.6%) and slow healing of cuts and wounds (88.5%) were the common symptoms of diabetes. Respondents knew that a family history of diabetes mellitus is the most significant risk factor, followed by being overweight /obesity (89.7%). Most respondents indicated that insulin injection (92%) and avoiding sugary foods (91.6%) were the best ways to control blood sugar. Even though work related to the medical field (p=001) and age (p=0.018) were significantly and positively associated with knowledge, the association of gender, income, level of education, medical field related education, nationality, and marital position with knowledge were not significant. Conclusions Our study findings has confirmed that respondents have good level of knowledge about diabetes mellitus (DM) and the associated risk factors, symptoms and chronic complications. Age and work related to the medical field have displayed a significant association with respondent’s knowledge about risk factors, symptoms and complications of diabetes.

The COVID-19 pandemic has greatly affected the personal and academic lives of undergraduates in Saudi Arabia. Although studies have suggested that COVID-19 increased the prevalence of psychological health problems among undergraduates, the associations between the risk of depression and safety practices and the influence of gender on these associations have not been studied in detail. A cross-sectional online survey was conducted among preparatory-year undergraduates in a large public university in Saudi Arabia during the outbreak. Depressive symptoms were assessed using the Center for Epidemiological Studies Depression (CES-D) Scale, and the practice of eight precautionary behaviors was also assessed. Data analysis was performed using the chi-square test, multiple linear regression and Spearman’s correlation coefficient. In total, 3044 undergraduates were surveyed. The mean age was 18.6 years (SD = 0.84), and 61.9% (n = 1883) of the participants were female. Overall, 47.7% of the participants reported having elevated depressive symptoms. Overall mean values of CES-D scores were higher among female undergraduates than that of male undergraduates (18.08 versus 15.56, p < 0.01). There were inverse and weak but significant relationships between the CES-D score and frequent cleaning of hands (male: r = −0.116, p < 0.01; female: r = −0.098, p < 0.01), wearing a mask when going out (male: r = −0.172, p < 0.01; female: r = −0.135, p < 0.01), keeping social distance (male: r = −0.117, p < 0.01; female: r = −0.147, p < 0.01), and covering the nose when sneezing (male: r = −0.202, p < 0.01; female: r = −0.115, p < 0.01). Regression analysis indicated that adherence to precautionary measures was a strong predictor of reduction of depressive symptoms in the target population. Male gender was also found to be an independent predictor of reduction of depressive symptoms. Depressive symptoms were highly prevalent in this target group, and female undergraduates seemed to be more vulnerable to developing such symptoms. Results also indicated that female undergraduates were more likely to implement the protective measures for COVID-19. The promotion of precautionary measures seems to be effective in reducing distress in this target population, but further research is needed to confirm our assertions.