Objective This study is aimed at assessing the perception and worries of Saudi healthcare providers about the application of artificial intelligence (AI) in Saudi healthcare facilities. Methods The study adopted a cross-sectional study involving 1026 Saudi healthcare providers between January 2023 and April 2023. The target population was healthcare providers across Saudi health facilities. Online questionnaires were administered through social media platforms. Data were analyzed using SPSS Statistics, version 26.0 (IBM Corp., Armonk, NY) to obtain important insights. Results The results of this study indicated that more than half (55.2%) of the respondents had good knowledge of AI, with (48.1%) of them being familiar with the application of AI in their specialty. A good proportion of the participants (57.9%) knew at least one term about the difference between machine learning and deep learning. More than half (69.9%) of the participants indicated that they had at one point in time used speech recognition or transcription application in their work. A large section (73.3%) of healthcare providers believed that AI would replace them at their job. A vast majority (84.9%) of the participants agreed that collaboration between medical schools with engineering and computer science faculties could be a game changer to provide a road for incorporating AI into medical curricula. The mean perception of AI in this study was 37.6 (SD=8.41; range 0-241). Age, level of health, health profession, and working experience all significantly impacted the positive perception score (p=0.021; p=0.031; p=0.041; p=0.026). However, there was no significant association between gender, nationality, and Saudi regions with a mean positive perception score. Conclusion There was a positive perception of AI among Saudi healthcare providers. Even though a substantial majority of Saudi healthcare providers were worried that AI would replace their jobs, the study revealed that AI serves as a crucial practitioner’s tool rather than a physician’s replacement.

Background and Objectives: Emotional intelligence (EI) plays a critical role in safeguarding the emotional and psychological well-being of healthcare workers, acting as a buffer against burnout, and influencing the quality of patient care. Despite its significance, there remains a need to understand how EI levels correlate with burnout and what factors predict burnout in high-stress healthcare environments. This study, conducted at King Khaled Hospital in Al-Majmaah, Saudi Arabia, aims to assess the EI levels of healthcare staff, to determine the relationship between EI and burnout, and to identify key predictors of burnout to inform targeted interventions for improving workforce resilience and patient outcomes. Materials and Methods: Both self-reporting and standardized tests were integrated using cross-sectional surveys to evaluate the EI of each participant and the burnout they experience by averaging the rating of a 30-item questionnaire, allowing comparison of the interaction between EI, burnout, and work factors. Results: A significantly moderate level of EI was identified, while a high level of well-being was associated with a low level of burnout, and a high level of emotionality was associated with a high level of burnout. Results indicated that high job demands, call rotation, or casual work, and insufficient staff support were organizational correlates of burnout. Conclusions: Improving EI with a focus on the well-being sub-dimension may prevent burnout, and, for that, the interventions must be specific at both personal and organizational levels.

Purpose: This study aimed to measure the effect of a virtual patient simulator (VPS) on medical students’ clinical reasoning skills using the Script Concordance Test (SCT). Methods: A quasi-experimental, posttest-only control group design was employed. Fourth-year medical students at Jouf University were randomly assigned to either an intervention group (n=46), which used the InSimu Patient simulator, or a control group (n=46), which received traditional teaching methods. Both groups completed an SCT post-intervention, and their scores were compared to those of an expert panel (n=12). The SCT assessed diagnostic, investigative, and treatment-related reasoning across 20 internal medicine cases. Cronbach’s Alpha was calculated to evaluate the reliability of the SCT.Results: The intervention group demonstrated significantly higher SCT scores compared to the control group, particularly in diagnostic and treatment-related questions (p < 0.05). The SCT showed good reliability (Cronbach’s Alpha = 0.85). Post-hoc analysis revealed significant differences between the intervention group and experts in several cases, indicating areas for further improvement. The control group also showed deviations from expert reasoning, highlighting the limitations of traditional teaching methods.Conclusion: Virtual patient simulators significantly enhance medical students’ clinical reasoning skills, as measured by the SCT. The findings support the integration of VPS into medical curricula to bridge the gap between theoretical knowledge and practical application. Future research should explore long-term outcomes and optimal implementation strategies for simulation-based learning.

Background: Healthcare disparity refers to the unequal burden of diseases, disability, injury, or mortality that certain groups experience compared to others. Healthcare providers must stay informed about the segments of society affected by specific health disparities. This study aimed to evaluate cultural competence attitudes to mitigate Healthcare Disparities among healthcare workers in the Aljouf region; we strive to enhance medical education.Methodology: An analytical cross-sectional study was conducted with 307 healthcare workers from various categories and sectors. A bilingual, standardised, and validated tool was used to evaluate three domains of healthcare disparity: Domain I - Cultural Competence Rationale, Context, and Definition; Domain II - Key Aspects of Cultural Competence; and Domain III - Impact of Stereotyping on Medical Decision-Making. Appropriate statistical tests, including the Mann-Whitney U test, Kruskal-Wallis test, and Spearman’s rank correlation, were employed to examine relationships between domains and participants' sociodemographic characteristics.Results: Less than 50% agreement was noted in most items across the three domains. The lack of attendance at social justice courses significantly correlated with Domain II (p = 0.004) and Domain III (p = 0.001). Positive correlations were observed between the domains: Domain I and Domain II (rho = 0.642, p < 0.001), Domain I and Domain III (rho = 0.674, p < 0.001), and Domain II and Domain III (rho = 0.823, p < 0.001).Conclusion: This study evaluated northern Saudi healthcare workers’ cultural competence attitudes, revealing limited support across domains. It recommends integrating cultural competence training and a multicenter survey of qualitative disparities.