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Background and Objectives Hand and wrist fractures are among the most frequently encountered injuries in emergency departments and are often misdiagnosed, particularly when interpreted by non-specialist clinicians. These diagnostic errors can lead to treatment delays and long-term complications. Artificial intelligence (AI), particularly deep learning algorithms, is emerging as a promising adjunct to improve diagnostic accuracy in radiographic fracture detection. This study aims to evaluate the effectiveness of Artificial Intelligence (AI) in detecting hand and wrist fractures compared to manual radiographic interpretation by clinicians. Materials and Methods A systematic review and meta-analysis were conducted to assess the diagnostic performance of AI models in detecting hand and wrist fractures compared to conventional radiographic interpretation by clinicians. A comprehensive search of PubMed, Google Scholar, Science Direct, and Wiley Online Library was performed. Eligible studies included those utilizing AI for fracture detection with sensitivity and specificity data. Pooled estimates were calculated using fixed- and random-effects models. Heterogeneity was assessed via I 2 statistics, and publication bias was examined using funnel plots and Egger’s test. Results Eighteen studies met inclusion criteria. The pooled sensitivity and specificity under the random-effects model were 0.910 and 0.912, respectively, indicating high diagnostic accuracy of AI models. However, substantial heterogeneity (I 2 = 99.09% for sensitivity; 96.43% for specificity) and publication bias were observed, likely due to variations in AI algorithms, sample sizes, and study designs. Conclusions Most AI models demonstrated good diagnostic accuracy, with high sensitivity and specificity scores (≥90%). However, some models fell short in sensitivity and specificity (≤90%), indicating performance variations across different AI models or algorithms. From a clinical perspective, AI models with lower sensitivity scores may fail to detect hand and wrist fractures, potentially delaying treatment, while those with lower specificity scores could lead to unnecessary interventions—treating hands and wrists that are not fractured.

Background: Electronic cigarettes are immensely popular among youths across the globe. However, knowledge, attitudes, and perceptions regarding their use vary by country. The present study investigated the knowledge and attitudes toward e-cigarette use among first-year university students in Saudi Arabia. Methods: A cross-sectional design was adopted, and an online, self-administered questionnaire assessing the knowledge of and attitudes toward e-cigarette use was utilized to conduct this study. The study population included students from all streams enrolled in their first year of university. Descriptive statistics were used to report percentages and frequencies, while advanced statistics, such as multiple logistic regression analyses, were used to determine associations. Results: The lifetime and current prevalence of e-cigarette use was 27.4% and 13.5%, respectively, among first-year university students. The mean age of smoking initiation was 16.4 ± 1.2 years. Of e-cigarette users, 31.3% smoked every day and 86.7% used flavored e-cigarettes. Knowledge of the harmful effects of e-cigarettes was high (addiction, 61.2%; asthma, 61%; nicotine content, 75.2%). However, when comparing e-cigarettes to regular cigarettes, only 22.5% and 48.4% of the students reported that they carry the same risk and contain the same chemicals as regular cigarettes do. There was a lack of knowledge (17.1%) regarding government regulations related to e-cigarettes. An attitude of support was observed regarding banning e-cigarettes (2.6 ± 1.5 on a scale of 0 to 4), while at the same time, some associated e-cigarette use with helping to reduce tobacco dependency (2.1 ± 1.2). Marketing adverts were agreed upon to positively influence youth (1.9 ± 1.4). However, the participants’ perceptions relating e-cigarette use to style were not well articulated. Significant gender differences were found: most of the women who participated in the study had better knowledge of e-cigarettes (p < 0.001). Being male, having higher income status (OR = 1.67; p = 0.013), being a current smoker (OR = 11.6; p < 0.001), and having intention for future use (OR = 3.45; p < 0.001) were strong predictors of e-cigarette use. Conclusions: These findings suggested the increasing popularity of e-cigarette use among male first-year university students. More educational campaigns and stricter regulations are needed to curb this trend.