Background: There is limited data that investigates the national rates of drug-resistant tuberculosis (TB) in Saudi Arabia.This study aimed to estimate the rates of multi-drug-resistant tuberculosis (MDR-TB), rifampicin-resistant tuberculosis (RR-TB), and monoresistance (MR) in Saudi Arabia.Methods: A retrospective cohort study was conducted on all TB cases reported to the National TB Control and Prevention Program (NTCPP) registry at the Saudi Ministry of Health between January 1, 2014 and December 31, 2015. A total of 2098 TB patients with positive TB cultures were included in the study. Subgroup analyses and multivariate binary logistic regression models were performed with IBM SPSS 23.0.Results: Of the total TB cases, 4.4% (95% CI: 3.59%–5.40%) were found to have MDR-TB. The rates of MR were 3.8% (95% CI: 2.99%–4.67%) for ethambutol, 5.4% (95% CI: 4.50%–6.49%) for pyrazinamide, 10.2% (95% CI: 5.89%–11.52%) for isoniazid, 11% (95% CI: 9.70%–12.43%) for streptomycin, and 5.9% (95% CI: 4.90%–6.96%) for rifampicin. The high rates of MDR and RR-TB were found among the younger age group, female gender, and those who had a previous history of TB. We also discovered that renal failure tends to increase the risk of rifampicin resistance.Conclusions: National TB data in Saudi Arabia shows that the rate of MDR-TB was similar to the global rate reported by the World Health Organization (WHO). It is a relatively high rate as compared to Western countries. The proportion of MDR/RR-TB patients tends to be higher in the younger age group, female gender, and in patients with a previous history of TB treatment. Effective strategies for prevention of all multi-drug-resistant TB cases are warranted.

Artificial intelligence (AI) is increasingly positioned as a transformative force in healthcare. The translation of AI from technical validation to real-world clinical impact remains a critical challenge. This systematic review aims to synthesize the evidence on the AI translational pathway in healthcare, focusing on the systemic barriers and facilitators to integration. Following PRISMA 2020 guidelines, we searched PubMed, Scopus, Web of Science, and IEEE Xplore for studies published between 2000 and 2025. We included peer-reviewed original research, clinical trials, observational studies, and reviews reporting on AI technical validation, clinical deployment, implementation outcomes, or ethical governance. While AI models consistently demonstrate high diagnostic accuracy (92–98% in radiology) and robust predictive performance (AUC 0.76–0.82 in readmission forecasting), clinical adoption remains limited, with only 15–25% of departments integrating AI tools and approximately 60% of projects failing beyond pilot testing. Key barriers include interoperability limitations affecting over half of implementations, lack of clinician trust in unsupervised systems (35%), and regulatory immaturity, with only 27% of countries establishing AI governance frameworks. Moreover, performance disparities exceeding 10% were identified in 28% of models, alongside a pronounced global divide, as 73% of low-resource health systems lack enabling infrastructure. These findings underscore the need for systemic, trustworthy, and equity-driven AI integration strategies.

Background: Bacteria in urine, which is known as bacteriuria, is divided clinically into two types: symptomatic, where the patient experiences urinary complaints, and asymptomatic (ASB), in which the patient has no complaints. Pregnant women who have ASB may later develop symptomatic urinary tract infection, which is considered one of the most common bacterial infections in pregnancy and which, if untreated, can result in serious adverse pregnancy outcomes. Objective: The goal of this research was to find out the prevalence of bacteriuria and its related factors among pregnant women in the Jazan Region, Saudi Arabia, recognize correlated microbial organisms, and identify the antimicrobial profile. Methods: A cross‐sectional study was conducted at Jazan and Sabyia general hospitals from June 28, 2023 to June 28, 2024. A systematic random sample of 392 pregnant women was selected. Urine samples were collected from them for bacteriological cultures and antimicrobial sensitivity testing. Data were collected and analyzed. Results: Out of 392 participants, 19% had bacteriuria. Most women were aged 26–35, overweight, and resided in urban areas. The most common bacteria found were Escherichia coli (39.2%) and Klebsiella pneumoniae (17.6%). Antimicrobial sensitivity was highest for cefuroxime and vancomycin and lowest for erythromycin and penicillin. Significant predictors of bacteriuria were residence, body mass index, gestational age, hemoglobin levels, and hemoglobin A1c levels. Conclusion: The study revealed a significant prevalence of bacteriuria among pregnant women in Jazan Region, highlighting the importance of routine screening and targeted interventions for high‐risk groups. Effective antibiotic stewardship programs are essential to managing resistance patterns.