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Objectives We aimed to assess undergraduate medical students’ knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine. Methods A multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses. Results Of the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies. Conclusions Arab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum. Clinical relevance statement This study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula. Key Points • Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology . • Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea . • Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training .

ObjectivesWe aimed to assess undergraduate medical students’ knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine.MethodsA multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses.ResultsOf the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies.ConclusionsArab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum.Clinical relevance statementThis study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula.Key Points• Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology.• Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea.• Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training.

Diabetic neuropathy (DN) and nephropathy (DKD) are prevalent microvascular complications in Type 2 diabetes mellitus (T2DM), often evolving silently. Detecting early nephropathy remains a clinical challenge, especially in patients with preserved renal function. The objective was to determine whether the Toronto Clinical Scoring System (TCS) for diabetic neuropathy can predict early nephropathy (albuminuria) in people with well-controlled T2DM who have a normal eGFR. We conducted a cross-sectional study with 122 T2DM patients (HbA1c < 7%, eGFR > 90) to look for peripheral neuropathy using TCS and nephropathy using the urinary albumin-to-creatinine ratio (UACR). Patients were classified based on the presence of albuminuria (UACR ≥ 30 mg/g). Statistical analyses included -tests, chi-square tests, Spearman correlation, and logistic regression. Patients with diabetic nephropathy or neuropathy were significantly older and exhibited higher systolic blood pressure and albuminuria. A clear stepwise increase in albuminuria was observed with rising neuropathy severity, with nephropathy prevalence ranging from 42% in patients without neuropathy to 72% in those with severe neuropathy. A significant positive correlation between TCS and UACR ( = 0.29, = 0.0012) supports a progressive link between nerve and kidney involvement. Clinical diabetic neuropathy is significantly associated with early nephropathy in well-controlled T2DM patients. Routine neuropathy assessment may serve as a simple, cost-effective predictor of subclinical renal damage. Future prospective studies should investigate whether early intervention in patients with neuropathy can attenuate or delay renal injury and whether this predictive link holds true across diverse ethnic and age groups.

To improve our local data and demographics of thyroid neoplasm in Makkah region, Kingdom of Saudi Arabia and provide some basic statistics for future studies in our local community.  Methods: A record based retrospective epidemiological study was conducted and included 314 thyroid disease patients who were presented to our centers at Makkah region, Kingdom of Saudi Arabia between December 2009 and December 2019. Results: A descriptive statistical analysis was carried out. The average age was 42.77 years, with a female-to-male ratio of 3:1, and most of the patients were Saudi (77%). Fifty-seven percent of cases were benign, while in malignant cases, 33.4% were papillary thyroid carcinoma. The mean follow-up time was 15.44 months, with excellent compliance in 39.4% of the patients.   Conclusion: Thyroid tumors have a leading incidence in head and neck tumors in Makkah, Kingdom of Saudi Arabia, mandating further studies to determine the causes and distribution in other regions of the country.

To evaluate the accuracy and e cacy of ne-needle aspiration cytology (FNAC) in diagnosing thyroid nodules, correlating it with the histopathological findings. A retrospective evaluation of 314 patients was undertaken at a tertiary referral center of King Abdullah Medical City (KAMC), Makkah, Kingdom of Saudi Arabia, between 2010-2019. Patients who presented with thyroid swellings underwent ultrasonography and FNAC. If indicated, surgery was performed. The FNAC findings were compared to the final histopathological reports. The findings for FNAC from our data set of 314 patients showed a sensitivity value of 79.8%, specificity of 82.1%, accuracy of 74.8%, positive predictive value of 74.8%, and negative predictive value of 85.9%. Conclusion: Our study showed that FNAC has high sensitivity and speci city in the initial evaluation of patients with thyroid nodules. When guided by ultrasonography, the accuracy can be markedly improved. Molecular markers once widely available can improve the diagnostic power of FNAC to be no less than the histopathologic evaluation of thyroid tissue.

Pain is a prevalent complaint in emergency departments (EDs) worldwide. Traditional pharmacological methods for pain relief, such as opioids and non-steroidal anti-inflammatory drugs (NSAIDs), have notable side effects and risks. Transcutaneous electrical nerve stimulation (TENS) is a non-pharmacological alternative that has shown promise in various clinical settings. This systematic review and meta-analysis aimed to evaluate the effectiveness of TENS for pain management and other outcomes in ED settings. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was conducted across six major databases: PubMed, Web of Science (WOS), Scopus, and Cochrane Central Register of Controlled Trials (CENTRAL), from inception until June 25, 2024. Randomized clinical trials involving the use of TENS in ED settings were included. Data extraction and quality assessment were performed independently by two reviewers, with conflicts resolved by a third reviewer. The search yielded 3,569 papers, of which 2,889 were screened after removing duplicates. Thirteen full-text articles were reviewed, and seven studies met the inclusion criteria for qualitative synthesis, with five of these suitable for meta-analysis. The studies demonstrated that TENS significantly reduced pain, heart rate, and the requirement for rescue medication in some cases, while also improving patient satisfaction and overall well-being. However, no significant changes were observed in blood pressure. The quality of the included studies varied, with some failing to meet the criteria for blinding and intention-to-treat analysis. TENS is an effective non-pharmacological intervention for pain management in ED settings, with additional benefits such as reduced heart rate and increased patient satisfaction. Further high-quality randomized controlled trials are necessary to confirm these findings and better understand the potential of TENS in acute care environments.

The role of gut microbiota in influencing the efficacy and side effect profile of biological therapies for autoimmune diseases has gained increasing attention. Understanding these interactions is crucial for optimizing treatment outcomes and minimizing adverse events associated with biological therapies. This systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We comprehensively analyzed studies involving human subjects with autoimmune diseases treated with biological therapies. Data on gut microbiota composition, therapeutic efficacy, and side effect profiles were extracted and synthesized to assess the impact of microbiota on treatment outcomes. Our review identified a significant relationship between gut microbiota composition and the efficacy of biological therapies. Specific bacterial taxa, such as Clostridiales and , were associated with improved therapeutic responses, while alterations in microbiota were linked to increased adverse events. The predictive potential was demonstrated with microbiota signatures correlating with treatment success and side effects, highlighting the relevance of microbial profiles in therapeutic outcomes. The findings suggest that gut microbiota plays a pivotal role in modulating the efficacy and side effect profile of biological therapies for autoimmune diseases. Integrating microbiota assessments into clinical practice could enhance personalized treatment strategies and improve patient outcomes.

Purpose To identify the public level of knowledge about the common ophthalmological conditions in Saudi Arabia. Methods We searched Medline, Embase, and CENTRAL for relevant literature. We included questionnaire-based cross-sectional studies performed in Saudi Arabia assessing the public awareness and attitude about general knowledge, causes/risk factors, signs/symptoms, disabilities/consequences, and relieving/management measures of the common ophthalmological conditions including glaucoma, cataract, and diabetic retinopathy (DR). The meta-analysis was performed on outcomes reported in ≥ 2 studies utilizing the random-effects model. Quality assessment was done using the Appraisal tool for Cross-Sectional Studies (AXIS) tool. Results Twenty-eight studies were deemed eligible for inclusion in this review. A total of 72 questions were reported in ≥ 2 studies and were included in the meta-analysis. The total number of participants was 14,408. The meta-analysis estimated that 57.63% (95% confidence interval (CI) 56.87–60.07%), 69.90% (95% CI 67.02–76.07%), and 68.65% (95% CI 65.94–71.23%) of the Saudi public have you ever heard or read about glaucoma, cataract, and DR, respectively. Of the public surveyed in the included studies, 43.68% (95% CI 41.54–45.85%), 55.43% (95% CI 54.03–56.82%), and 63% (95% CI 60.8–65.1%) believed that glaucoma, cataract, and DR could be treated. Conclusion This systematic review showed that the level of knowledge among the Saudi population about the common ophthalmological conditions was the highest with respect to cataract, followed by DR and glaucoma. The areas of unsatisfactory level of awareness about the common ophthalmological conditions included risk factors, signs/symptoms, complications, and management options. These areas need to be addressed appropriately by future educational interventions.

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.