Exploring behavioral intention to use telemedicine services post COVID-19: a cross sectional study in Saudi Arabia.

Introduction: While telemedicine offers significant benefits, there remain substantial knowledge gaps in the literature, particularly regarding its use in Saudi Arabia. This study aims to explore health consumers' behavioral intention to use telemedicine examining the associated factors such as eHealth literacy and attitudes toward telemedicine services. Methods: A cross-sectional observational study was conducted to collect data on demographics, health status, internet skills, attitudes toward telemedicine, and eHealth literacy. An online survey was administered at two large public gatherings in Riyadh. The eHEALS-Pl scale was used to measure perceived eHealth literacy levels, and data analysis was performed using SPSS (IBM Corp. United States). Results: There were 385 participants, with an equal distribution of genders. The largest age group was 18-20 years old (57%). Nearly half of the participants were neither employed nor students, while 43% had access to governmental hospitals through employment. 71% reported proficiency in using the internet. Health-wise, 47% rated their health as excellent, and 56% did not have medical insurance. 87% expressed a high likelihood of using telemedicine if offered by a provider. Participants were categorized based on their eHealth Literacy scores, with 54% scoring low and 46% scoring high. Overall, participants showed positive attitudes toward telemedicine, with 82% agreeing that it saves time, money, and provides access to specialized care. About half of the participants perceived the process of seeing a doctor through telemedicine video as complex. Both eHealth Literacy and attitudes toward telemedicine showed a statistically significant association with the intention to use telemedicine (p < 0.001). There was a positive and significant correlation between eHealth Literacy and attitudes (ρ =0.460; p < 0.001). Multivariate ordinal regression analysis revealed that the odds for a high likelihood of intention to use telemedicine significantly increased with positive attitudes (p < 0.001). Mediation analysis confirmed the significant mediating role of attitudes toward telemedicine in the relationship between eHealth Literacy and the intention to use telemedicine. Conclusion: The findings underline the importance of enhancing health literacy and consumer attitudes toward telemedicine, particularly during the healthcare digital transformation we are experiencing globally. This is crucial for promoting increased acceptance and utilization of telemedicine services beyond the COVID-19 pandemic.

Art or Artifact: Evaluating the Accuracy, Appeal, and Educational Value of AI-Generated Imagery in DALL·E 3 for Illustrating Congenital Heart Diseases.

Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility.

ChatGPT-3.5 System Usability Scale early assessment among Healthcare Workers: Horizons of adoption in medical practice.

Artificial intelligence (AI) chatbots, such as ChatGPT, have widely invaded all domains of human life. They have the potential to transform healthcare future. However, their effective implementation hinges on healthcare workers' (HCWs) adoption and perceptions. This study aimed to evaluate HCWs usability of ChatGPT three months post-launch in Saudi Arabia using the System Usability Scale (SUS). A total of 194 HCWs participated in the survey. Forty-seven percent were satisfied with their usage, 57 % expressed moderate to high trust in its ability to generate medical decisions. 58 % expected ChatGPT would improve patients' outcomes, even though 84 % were optimistic of its potential to improve the future of healthcare practice. They expressed possible concerns like recommending harmful medical decisions and medicolegal implications. The overall mean SUS score was 64.52, equivalent to 50 % percentile rank, indicating high marginal acceptability of the system. The strongest positive predictors of high SUS scores were participants' belief in AI chatbot's benefits in medical research, self-rated familiarity with ChatGPT and self-rated computer skills proficiency. Participants' learnability and ease of use score correlated positively but weakly. On the other hand, medical students and interns had significantly high learnability scores compared to others, while ease of use scores correlated very strongly with participants' perception of positive impact of ChatGPT on the future of healthcare practice. Our findings highlight the HCWs' perceived marginal acceptance of ChatGPT at the current stage and their optimism of its potential in supporting them in future practice, especially in the research domain, in addition to humble ambition of its potential to improve patients' outcomes particularly in regard of medical decisions. On the other end, it underscores the need for ongoing efforts to build trust and address ethical and legal concerns of AI implications in healthcare. The study contributes to the growing body of literature on AI chatbots in healthcare, especially addressing its future improvement strategies and provides insights for policymakers and healthcare providers about the potential benefits and challenges of implementing them in their practice.

Awareness, Attitudes, and Willingness: A Cross-Sectional Study of Organ Donation in Saudi Arabia.

BACKGROUND: Organ transplantation is inherently dependent on the availability of organ donors. There is a noticeable paucity of literature addressing the rates of organ donation registration and the awareness of Islamic regulations (Fatwa) regarding organ donation within Saudi Arabia. Our study aimed to evaluate the level of organ donation registration, awareness of Islamic regulations, and knowledge of the Saudi Center for Organ Transplantation (SCOT) within the Saudi society. METHODS: We conducted a cross-sectional survey from 30 March to 9 April 2023. This survey aimed to assess the awareness of Islamic (Fatwa) guidance on organ donation, the role of SCOT, and the rate of organ donation registration facilitated through the Tawakkalna app, the official health passport application in Saudi Arabia. RESULTS: Out of 2329 respondents, 21% had registered as potential deceased organ donors, despite 87% acknowledging the importance of organ donation. Awareness of the Islamic Fatwa regarding organ donation was reported by 54.7% of respondents, and 37% recognized the Fatwa's acceptance of brain death criteria. The likelihood of registration as organ donors was higher among Saudi citizens under 45 years of age, females, healthcare workers (HCWs), individuals with higher education, relatives of patients awaiting organ donations, those informed about the Islamic Fatwas, and those willing to donate organs to friends. Conversely, being over the age of 25, Saudi nationality, employment as an HCW, awareness of SCOT, and prior organ donation registration were predictive of a heightened awareness of Islamic Fatwas. However, perceiving the importance of organ donation correlated with a lower awareness of the Fatwas. Significant positive correlations were found between awareness of SCOT, awareness of Fatwas, and registration for organ donation. CONCLUSIONS: While the Saudi population exhibits a high regard for the importance of organ donation, this recognition is not adequately translated into registration rates. The discrepancy may be attributable to limited awareness of SCOT and the relevant Islamic Fatwas. It is imperative to initiate organ donation awareness campaigns that focus on religious authorization to boost organ donation rates and rectify prevalent misconceptions.

ChatGPT Surpasses 1000 Publications on PubMed: Envisioning the Road Ahead.

The exponential growth of ChatGPT in medical literature, amassing over 1000 PubMed citations by August 2023, underscores a pivotal juncture in the convergence of artificial intelligence (AI) and healthcare. This remarkable rise not only showcases its potential to revolutionize medical academia but also indicates its impending influence on patient care and healthcare systems. Notwithstanding this enthusiasm, one-third of these citations are editorials or commentaries, stressing a gap in empirical research. Alongside its potential, there are concerns about ChatGPT becoming a "Weapon of Mass Deception" and the need for rigorous evaluations to counter inaccuracies. The World Association of Medical Editors has released guidelines emphasizing that AI tools should not be manuscript co-authors and advocates for clear disclosures in AI-assisted academic works. Interestingly, ChatGPT achieved its citation milestone within nine months, compared to Google's 14 years. As Large Language Models (LLMs), like ChatGPT, become more integral in healthcare, issues surrounding data protection, patient privacy, and ethical implications gain prominence. As the future of LLM research unfolds, key areas of interest include its efficacy in clinical settings, its role in telemedicine, and its potential in medical education. The journey ahead necessitates a harmonious partnership between the medical community and AI developers, emphasizing both technological advancements and ethical considerations.

Healthcare's New Horizon With ChatGPT's Voice and Vision Capabilities: A Leap Beyond Text.

The integration of artificial intelligence (AI) in healthcare is responsible for a paradigm shift in medicine. OpenAI's recent augmentation of their Generative Pre-trained Transformer (ChatGPT) large language model (LLM) with voice and image recognition capabilities (OpenAI, Delaware) presents another potential transformative tool for healthcare. Envision a healthcare setting where professionals engage in dynamic interactions with ChatGPT to navigate the complexities of atypical medical scenarios. In this innovative landscape, practitioners could solicit ChatGPT's expertise for concise summarizations and insightful extrapolations from a myriad of web-based resources pertaining to similar medical conditions. Furthermore, imagine patients using ChatGPT to identify abnormalities in medical images or skin lesions. While the prospects are diverse, challenges such as suboptimal audio quality and ensuring data security necessitate cautious integration in medical practice. Drawing insights from previous ChatGPT iterations could provide a prudent roadmap for navigating possible challenges. This editorial explores some possible horizons and potential hurdles of ChatGPT's enhanced functionalities in healthcare, emphasizing the importance of continued refinements and vigilance to maximize the benefits while minimizing risks. Through collaborative efforts between AI developers and healthcare professionals, another fusion of AI and healthcare can evolve into enriched patient care and enhanced medical experience.

Integrating ChatGPT in Medical Education: Adapting Curricula to Cultivate Competent Physicians for the AI Era.

The rapid advancements in artificial intelligence (AI) language models, particularly ChatGPT (OpenAI, San Francisco, California, United States), necessitate the adaptation of medical education curricula to cultivate competent physicians in the AI era. In this editorial, we discuss short-term solutions and long-term adaptations for integrating ChatGPT into medical education. We recommend promoting digital literacy, developing critical thinking skills, and emphasizing evidence-based relevance as quick fixes. Long-term adaptations include focusing on the human factor, interprofessional collaboration, continuous professional development, and research and evaluation. By implementing these changes, medical educators can optimize medical education for the AI era, ensuring students are well prepared for a technologically advanced future in healthcare.

Prediction of Emergency Cesarean Section Using Detectable Maternal and Fetal Characteristics Among Saudi Women.

Background: The worldwide rate of cesarean section (CS) is increasing. Development of prediction models for a specific population may improve the unmet need for CS as well as reduce the overuse of CS. Objective: To explore risk factors associated with emergency CS, and to determine the accuracy of predicting it. Methods: A retrospective analysis of the medical records of women who delivered between January 1, 2021-December 2022 was conducted, relevant maternal and neonatal data were retrieved. Results: Out of 1793 deliveries, 447 (25.0%) had emergency CS. Compared to control, the risk of emergency CS was higher in primiparous women (OR 2.13, 95% CI 1.48 to 3.06), in women with higher Body mass index (BMI) (OR 1.77, 95% CI 1.27 to 2.47), in association with history of previous CS (OR 4.81, 95% CI 3.24 to 7.15) and in women with abnormal amniotic fluid (OR 2.30, 95% CI 1.55 to 3.41). Additionally, women with hypertensive disorders had a 176% increased risk of emergency CS (OR 2.76, 95% CI 1.35-5.63). Of note, the risk of emergency CS was more than three times higher in women who delivered a small for gestational age infant (OR 3.29, 95% CI 1.93-5.59). Based on the number of risk factors, a prediction model was developed, about 80% of pregnant women in the emergency CS group scored higher grades compared to control group. The area under the curve was 0.72, indicating a good discriminant ability of the model. Conclusion: This study identified several risk factors associated with emergency CS in pregnant Saudi women. A prediction model showed 72% accuracy in predicting the likelihood of emergency CS. This information can be useful to individualize the risk of emergency CS, and to implement appropriate measures to prevent unnecessary CS.

ChatGPT and the Future of Digital Health: A Study on Healthcare Workers' Perceptions and Expectations.

This study aimed to assess the knowledge, attitudes, and intended practices of healthcare workers (HCWs) in Saudi Arabia towards ChatGPT, an artificial intelligence (AI) Chatbot, within the first three months after its launch. We also aimed to identify potential barriers to AI Chatbot adoption among healthcare professionals. A cross-sectional survey was conducted among 1057 HCWs in Saudi Arabia, distributed electronically via social media channels from 21 February to 6 March 2023. The survey evaluated HCWs' familiarity with ChatGPT-3.5, their satisfaction, intended future use, and perceived usefulness in healthcare practice. Of the respondents, 18.4% had used ChatGPT for healthcare purposes, while 84.1% of non-users expressed interest in utilizing AI Chatbots in the future. Most participants (75.1%) were comfortable with incorporating ChatGPT into their healthcare practice. HCWs perceived the Chatbot to be useful in various aspects of healthcare, such as medical decision-making (39.5%), patient and family support (44.7%), medical literature appraisal (48.5%), and medical research assistance (65.9%). A majority (76.7%) believed ChatGPT could positively impact the future of healthcare systems. Nevertheless, concerns about credibility and the source of information provided by AI Chatbots (46.9%) were identified as the main barriers. Although HCWs recognize ChatGPT as a valuable addition to digital health in the early stages of adoption, addressing concerns regarding accuracy, reliability, and medicolegal implications is crucial. Therefore, due to their unreliability, the current forms of ChatGPT and other Chatbots should not be used for diagnostic or treatment purposes without human expert oversight. Ensuring the trustworthiness and dependability of AI Chatbots is essential for successful implementation in healthcare settings. Future research should focus on evaluating the clinical outcomes of ChatGPT and benchmarking its performance against other AI Chatbots.

Harnessing Machine Learning in Early COVID-19 Detection and Prognosis: A Comprehensive Systematic Review.

During the early phase of the COVID-19 pandemic, reverse transcriptase-polymerase chain reaction (RT-PCR) testing faced limitations, prompting the exploration of machine learning (ML) alternatives for diagnosis and prognosis. Providing a comprehensive appraisal of such decision support systems and their use in COVID-19 management can aid the medical community in making informed decisions during the risk assessment of their patients, especially in low-resource settings. Therefore, the objective of this study was to systematically review the studies that predicted the diagnosis of COVID-19 or the severity of the disease using ML. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), we conducted a literature search of MEDLINE (OVID), Scopus, EMBASE, and IEEE Xplore from January 1 to June 31, 2020. The outcomes were COVID-19 diagnosis or prognostic measures such as death, need for mechanical ventilation, admission, and acute respiratory distress syndrome. We included peer-reviewed observational studies, clinical trials, research letters, case series, and reports. We extracted data about the study's country, setting, sample size, data source, dataset, diagnostic or prognostic outcomes, prediction measures, type of ML model, and measures of diagnostic accuracy. Bias was assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). This study was registered in the International Prospective Register of Systematic Reviews (PROSPERO), with the number CRD42020197109. The final records included for data extraction were 66. Forty-three (64%) studies used secondary data. The majority of studies were from Chinese authors (30%). Most of the literature (79%) relied on chest imaging for prediction, while the remainder used various laboratory indicators, including hematological, biochemical, and immunological markers. Thirteen studies explored predicting COVID-19 severity, while the rest predicted diagnosis. Seventy percent of the articles used deep learning models, while 30% used traditional ML algorithms. Most studies reported high sensitivity, specificity, and accuracy for the ML models (exceeding 90%). The overall concern about the risk of bias was "unclear" in 56% of the studies. This was mainly due to concerns about selection bias. ML may help identify COVID-19 patients in the early phase of the pandemic, particularly in the context of chest imaging. Although these studies reflect that these ML models exhibit high accuracy, the novelty of these models and the biases in dataset selection make using them as a replacement for the clinicians' cognitive decision-making questionable. Continued research is needed to enhance the robustness and reliability of ML systems in COVID-19 diagnosis and prognosis.

Age and Gender-Specific Pattern of Cardiovascular Disease Risk Factors in Saudi Arabia: A Subgroup Analysis from the Heart Health Promotion Study.

OBJECTIVE: To investigate gender and age-specific distribution patterns of cardiovascular disease risk factors in the Saudi population for tailored health policies. METHODS: From the heart health promotion study, 3063 adult Saudis were included in this study. The study cohort was divided into five age groups (less than 40 years, 40-45 years, 46-50 years, 51-55 years and ≥56 years). The prevalence of metabolic, socioeconomic, and cardiac risk was compared between the groups. Anthropometric and biochemical data were gathered using the World Health Organization stepwise approach to chronic disease risk factors. The cardiovascular risk (CVR) was determined using the Framingham Coronary Heart Risk Score. RESULTS: The prevalence of CVR risk increased with age in both genders. Both Saudi men and women exhibit similar propensities for sedentary lifestyles and unhealthy food habits. The prevalence of tobacco smoking was significantly higher and from an early age in males compared to females (28% and 2.7%, respectively, at age 18-29 years). There is no significant difference in either the prevalence of diabetes, hypertension, or metabolic syndrome between men and women before the age of 60 years. Old Saudi females (≥60 years) have a higher prevalence of diabetes (50% vs. 38.7%) and metabolic syndrome (55.9% versus 43.5%). Obesity was more prevalent in females aged 40-49 years onwards (56.2% vs. 34.9% males), with 62.9% of females aged ≥60 years being obese compared to 37.9% of males. Dyslipidaemia prevalence increased with the progression of age, significantly more in males than females. Framingham high-risk scores showed that 30% of males were at high risk of cardiovascular diseases at the age group of 50-59 years, while only 3.7% of the females were considered as such. CONCLUSIONS: Both Saudi men and women exhibit similar propensities for sedentary lifestyles and unhealthy food habits, with a marked increase in cardiovascular and metabolic risk factors with age. Gender differences exist in risk factor prevalence, with obesity as the main risk factor in women, while smoking and dyslipidaemia were the main risk factors in men.

Enhancing Expert Panel Discussions in Pediatric Palliative Care: Innovative Scenario Development and Summarization With ChatGPT-4.

This study presents a novel approach to enhance expert panel discussions in a medical conference through the use of ChatGPT-4 (Generative Pre-trained Transformer version 4), a recently launched powerful artificial intelligence (AI) language model. We report on ChatGPT-4's ability to optimize and summarize the medical conference panel recommendations of the first Pan-Arab Pediatric Palliative Critical Care Hybrid Conference, held in Riyadh, Saudi Arabia. ChatGPT-4 was incorporated into the discussions in two sequential phases: first, scenarios were optimized by the AI model to stimulate in-depth conversations; second, the model identified, summarized, and contrasted key themes from the panel and audience discussions. The results suggest that ChatGPT-4 effectively facilitated complex do-not-resuscitate (DNR) conflict resolution by summarizing key themes such as effective communication, collaboration, patient and family-centered care, trust, and ethical considerations. The inclusion of ChatGPT-4 in pediatric palliative care panel discussions demonstrated potential benefits for enhancing critical thinking among medical professionals. Further research is warranted to validate and broaden these insights across various settings and cultures.

ChatGPT-4 and the Global Burden of Disease Study: Advancing Personalized Healthcare Through Artificial Intelligence in Clinical and Translational Medicine.

The fusion of insights from the comprehensive global burden of disease (GBD) study and the advanced artificial intelligence of open artificial intelligence (AI) chat generative pre-trained transformer version 4 (ChatGPT-4) brings the potential to transform personalized healthcare planning. By integrating the data-driven findings of the GBD study with the powerful conversational capabilities of ChatGPT-4, healthcare professionals can devise customized healthcare plans that are adapted to patients' lifestyles and preferences. We propose that this innovative partnership can lead to the creation of a novel AI-assisted personalized disease burden (AI-PDB) assessment and planning tool. For the successful implementation of this unconventional technology, it is crucial to ensure continuous and accurate updates, expert supervision, and address potential biases and limitations. Healthcare professionals and stakeholders should have a balanced and dynamic approach, emphasizing interdisciplinary collaborations, data accuracy, transparency, ethical compliance, and ongoing training. By investing in the unique strengths of both ChatGPT-4, especially its newly introduced features such as live internet browsing or plugins, and the GBD study, we may enhance personalized healthcare planning. This innovative approach has the potential to improve patient outcomes and optimize resource utilization, as well as pave the way for the worldwide implementation of precision medicine, thereby revolutionizing the existing healthcare landscape. However, to fully harness these benefits at both the global and individual levels, further research and development are warranted. This will ensure that we effectively tap into the potential of this synergy, bringing societies closer to a future where personalized healthcare is the norm rather than the exception.

Mitigating the Burden of Severe Pediatric Respiratory Viruses in the Post-COVID-19 Era: ChatGPT Insights and Recommendations.

In the current post-pandemic era, the rapid spread of respiratory viruses among children and infants resulted in hospitals and pediatric intensive care units (PICUs) becoming overwhelmed. Healthcare providers around the world faced a significant challenge from the outbreak of respiratory viruses like respiratory syncytial virus (RSV), metapneumovirus, and influenza viruses. The chatbot generative pre-trained transformer, ChatGPT, which was launched by OpenAI in November 2022, had both positive and negative aspects in medical writing. Still, it has the potential to generate mitigation suggestions that could be rapidly implemented. We describe the generated suggestion from ChatGPT on 27 Feb 2023 in response to the question "What's your advice for the pediatric intensivists?" We as human authors and healthcare providers, do agree with and supplement with references these suggestions of ChatGPT. We also advocate that artificial intelligence (AI)-enabled chatbots could be utilized in seeking a vigilant and robust healthcare system to rapidly adapt to changing respiratory viruses circulating around the seasons, but AI-generated suggestions need experts to validate them, and further research is warranted.

Overview of Early ChatGPT's Presence in Medical Literature: Insights From a Hybrid Literature Review by ChatGPT and Human Experts.

ChatGPT, an artificial intelligence chatbot, has rapidly gained prominence in various domains, including medical education and healthcare literature. This hybrid narrative review, conducted collaboratively by human authors and ChatGPT, aims to summarize and synthesize the current knowledge of ChatGPT in the indexed medical literature during its initial four months. A search strategy was employed in PubMed and EuropePMC databases, yielding 65 and 110 papers, respectively. These papers focused on ChatGPT's impact on medical education, scientific research, medical writing, ethical considerations, diagnostic decision-making, automation potential, and criticisms. The findings indicate a growing body of literature on ChatGPT's applications and implications in healthcare, highlighting the need for further research to assess its effectiveness and ethical concerns.

Exploring Determinants of COVID-19 Vaccine Acceptance, Uptake, and Hesitancy in the Pediatric Population: A Study of Parents and Caregivers in Saudi Arabia during the Initial Vaccination Phase.

OBJECTIVES: This study aims to assess COVID-19 vaccine acceptance, uptake, and hesitancy among parents and caregivers of children in Saudi Arabia during the initial rollout of pediatric COVID-19 vaccination. METHODS: An electronic survey was used to collect data from participants who visited a COVID-19 vaccine center. The survey included demographic data, COVID-19 vaccine status among participants and their children, and reasons for vaccine acceptance or rejection. The Vaccine Hesitancy Scale (VHS) tool was also employed to assess vaccine hesitancy and attitudes toward the COVID-19 vaccine and routine childhood vaccination. Multivariate binary regression analysis was used to identify predictors of actual COVID-19 vaccine uptake among children. RESULTS: Of the 873 respondents included in the analysis, 61.5% were parents and 38.5% were other caregivers. Of the participants, 96.9% had received the COVID-19 vaccine. Six hundred and ninety-four participants accepted the vaccine for their children, with the main reasons being an endorsement by the Saudi Ministry of Health (60%) and the importance of going back to school (55%). One hundred and seventy-nine participants would not vaccinate their children, with the most common reasons being fear of adverse effects (49%) and inadequate data about vaccine safety (48%). Factors such as age, COVID-19 vaccination status, self-rated family commitment level, attitudes toward routine children's vaccines, and participants' generalized anxiety disorder (GAD7) score did not significantly correlate with children's COVID-19 vaccination status. Parents were less likely to vaccinate their children compared to other caregivers, and participants with a higher socioeconomic status were more likely to vaccinate their children. CONCLUSION: Vaccine acceptance and uptake were high during the initial pediatric COVID-19 vaccination rollout in Saudi Arabia. Still, the ongoing endorsement of the Ministry of Health and healthcare authorities should continue to advocate for better vaccine uptake in children.