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.

Decline in ESBL Production and Carbapenem Resistance in Urinary Tract Infections among Key Bacterial Species during the COVID-19 Pandemic

The COVID-19 pandemic has led to significant changes in healthcare practices, including increased antibiotic usage. This study aimed to investigate the impact of the pandemic on the prevalence of extended-spectrum β-lactamase (ESBL) production and carbapenem resistance among key bacterial species causing urinary tract infections (UTIs). Conducted at King Fahad Medical City in Riyadh from January 2018 to December 2022, the study analyzed urine samples from 9697 UTI patients. Patients were categorized into 'pre-COVID-19' and 'during COVID-19' groups. Bacterial isolates were identified, and antimicrobial susceptibility testing was performed following guidelines. ESBL production was detected using the Double-Disc Synergy Test. Escherichia coli and Klebsiella pneumoniae were the main pathogens. During the pandemic, ESBL production decreased in E. coli by 1.9% and in K. pneumoniae by 6.0%. Carbapenem resistance also declined, with E. coli displaying a 1.2% reduction and K. pneumoniae and Pseudomonas aeruginosa displaying 10.7% and 7.9% reductions, respectively. Notably, logistic regression analysis revealed that the odds of ESBL presence were 10% lower during the COVID-19 pandemic (OR 0.91; 95% CI 0.83-0.99; p = 0.040), and there was a significant reduction in the odds of carbapenem resistance (OR 0.43; 95% CI 0.37-0.51; p < 0.001). This study reveals a significant decrease in ESBL production and carbapenem resistance among UTI pathogens during the COVID-19 pandemic, hinting at the impact of modified antibiotic and healthcare approaches. It emphasizes the need for persistent antimicrobial resistance surveillance and policy adaptation to address resistance challenges, offering key directions for future public health actions.

Palliative Care in Saudi Arabia: An Updated Assessment Following the National Vision 2030 Reforms

Background: Palliative care (PC) plays a crucial role in improving the quality of life for terminally ill patients and their families. In Saudi Arabia, the Reform of Healthcare Vision 2030 has recognized the importance of PC and aimed to enhance its availability and quality. Objectives: This study evaluates the current state of PC in Saudi Arabia post-Vision 2030 reforms. Design: A cross-sectional survey-based research was conducted at a ministry of health health care facility to assess the accessibility and quality of PC services. Setting/Subjects: The survey collected quantitative and qualitative data from PC managers in Saudi Arabia. Retrospective analysis of annual death records determined the demand for PC. Results: The results indicate notable progress in developing PC services in Saudi Arabia, including increased number of PC units, community home care services, outpatient services, and consultations. However, challenges persist in terms of geographical distribution, resource allocation, and availability of pain medications, particularly opioids. The study highlights the substantial need for PC for both cancer and noncancer patients, emphasizing the importance of expanding these services. Conclusions: To further improve PC, policymakers and stakeholders should prioritize resource allocation, health care workforce, and access to pain medications. These efforts will address the growing demand for PC and benefit terminally ill patients and their families in Saudi Arabia.

Navigating Novel Uncertainties of COVID-19: The Rise of the JN.1 Variant

In the shadow of the coronavirus disease 2019 (COVID-19) pandemic, the emergence of the JN.1 variant highlights the need for continued vigilance. This Editorial examines the characteristics of JN.1, derived from BA.2.86, and how it affects global public health. Despite its mutation on the spike protein and rapid spread, there has been no increase in disease severity, particularly in terms of ICU admissions, as evidenced by data from the World Health Organization (WHO) and the Centers for Disease Control and Prevention (CDC). We emphasize the importance of continued surveillance, vaccine adaptation, and public health preparedness while advocating for a balanced response to effectively manage the post-pandemic era. It reflects on the resilience built through vaccination efforts and the need for international cooperation to navigate the way forward in the face of additional severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants.

Characteristics of Patients Undergoing Electrophysiologic Procedures in a Tertiary Hospital in Saudi Arabia

Introduction

The electrophysiology field has progressed rapidly over the last 2 decades. No study has examined the characteristics of patients and types of electrophysiology procedures performed in the Kingdom of Saudi Arabia. This is important given our distinctly different demographic composition and health system. As such, we sought to describe the characteristics of consecutive patients presenting for electrophysiology procedures in our tertiary care hospital.

Methods

Data was collected from the electrophysiology database at King Khalid University Hospital for procedures performed between April 2016 and November 2022. Patients' characteristics were retrieved from the electronic medical record. Procedures were categorized into supraventricular tachycardia, premature ventricular contraction and "complex ablations", which included atrial fibrillation and scar-mediated ventricular tachycardia ablation. If no abnormality was found, the procedure was labeled as "normal EP study". Multivariate regression analysis was performed to assess predictors of atrioventricular nodal reentry tachycardia among patients presenting with undifferentiated supraventricular tachycardia.

Results

A total of 459 patients were included in the study. The mean age was 42.06 years (±14.89 years), and 256 (55.77 %) were females. The most common procedure was supraventricular tachycardia (n = 289/459, 63.24 %), and only 5 % had complex ablations. The most common type of supraventricular tachycardia ablated was found to be atrioventricular nodal reentry tachycardia (n = 157/289, 54 %). Multivariate logistic regression revealed female sex and age to be independently associated with atrioventricular nodal reentry tachycardia (OR = 2.27 95 % CI [1.40-3.67]) for female sex and (OR = 1.02 95 % CI [1.01-1.04]) for every increase in age by 1-year.

Conclusion

We reported a younger average age than other countries and less complex ablations. In addition, we reported 2 independent predictors of atrioventricular nodal reentry tachycardia in patients presenting with undifferentiated supraventricular tachycardia. Larger studies including multiple centers should be performed to confirm our findings.

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.

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.

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.

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.

Snakebite Advice and Counseling From Artificial Intelligence: An Acute Venomous Snakebite Consultation With ChatGPT

BACKGROUND

Snakebites, particularly from venomous species, present a significant global public health challenge. Access to accurate and timely information regarding snakebite prevention, recognition, and management is crucial for minimizing morbidity and mortality. Artificial intelligence (AI) language models, such as ChatGPT (Chat Generative Pre-trained Transformer), have the potential to revolutionize the dissemination of medical information and improve patient education and satisfaction.

METHODS

This study aimed to explore the utility of ChatGPT, an advanced language model, in simulating acute venomous snakebite consultations. Nine hypothetical questions based on comprehensive snakebite management guidelines were posed to ChatGPT, and the responses were evaluated by clinical toxicologists and emergency medicine physicians.

RESULTS

ChatGPT provided accurate and informative responses related to the immediate management of snakebites, the urgency of seeking medical attention, symptoms, and health issues following venomous snakebites, the role of antivenom, misconceptions about snakebites, recovery, pain management, and prevention strategies. The model highlighted the importance of seeking professional medical care and adhering to healthcare practitioners' advice. However, some limitations were identified, including outdated knowledge, lack of personalization, and inability to consider regional variations and individual characteristics.

CONCLUSION

ChatGPT demonstrated proficiency in generating intelligible and well-informed responses related to venomous snakebites. It offers accessible and real-time advice, making it a valuable resource for preliminary information, education, and triage support in remote or underserved areas. While acknowledging its limitations, such as the need for up-to-date information and personalized advice, ChatGPT can serve as a supplementary source of information to complement professional medical consultation and enhance patient education. Future research should focus on addressing the identified limitations and establishing region-specific guidelines for snakebite management.

Artificial Intelligence (AI) Chatbots in Medicine: A Supplement, Not a Substitute

This editorial discusses the role of artificial intelligence (AI) chatbots in the healthcare sector, emphasizing their potential as supplements rather than substitutes for medical professionals. While AI chatbots have demonstrated significant potential in managing routine tasks, processing vast amounts of data, and aiding in patient education, they still lack the empathy, intuition, and experience intrinsic to human healthcare providers. Furthermore, the deployment of AI in medicine brings forth ethical and legal considerations that require robust regulatory measures. As we move towards the future, the editorial underscores the importance of a collaborative model, wherein AI chatbots and medical professionals work together to optimize patient outcomes. Despite the potential for AI advancements, the likelihood of chatbots completely replacing medical professionals remains low, as the complexity of healthcare necessitates human involvement. The ultimate aim should be to use technology like AI chatbots to enhance patient care and outcomes, not to replace the irreplaceable human elements of healthcare.

Bacterial Pathogens and Antimicrobial Susceptibility Patterns of Urinary Tract Infections in Children during COVID-19 2019-2020: A Large Tertiary Care Center in Saudi Arabia

BACKGROUND

One of the most prevalent bacterial infections in children is urinary tract infection (UTI), which has become a major concern with increasing resistance of the pathogens to the routinely used antimicrobial agents. The aim of the study is to determine the antimicrobial susceptibility patterns of pediatric UTI-causing pathogens, including ESBL-producing bacteria, in Saudi Arabia.

METHODS

This cross-sectional retrospective study was conducted to ascertain the frequency of isolation and the antimicrobial resistance pattern of uropathogens among children aged 0-15 years. The data from the urine cultures was collected during 2019-2020 at the King Fahad Medical City, a major tertiary hospital in Riyadh, Saudi Arabia. A total of 1022 urine samples from patients diagnosed with urinary tract infections (UTIs) were collected for this study. Microbial species present in the samples were cultured and identified using standard biochemical techniques. To assess the resistance of these strains to antimicrobial drugs, an in vitro method was employed, and the criteria set by the Clinical Laboratory Standard Institute (CLSI) were followed. In addition, a double-disc synergy test was conducted to identify strains of E. coli that produce extended-spectrum beta-lactamase (ESBL).

RESULTS

The predominant pathogens were E. coli (58.6%), followed by Klebsiella sp. (23.9%). E. coli isolates were more sensitive to meropenem and ertapenem in 99.2% of cases, followed by amikacin (99%). Klebsiella sp. were sensitive to amikacin in 97.1% of cases, followed by meropenem and ertapenem (92.2% in both). The highest sensitivities of antimicrobials toward ESBL were for meropenem and ertapenem (100% in both), followed by amikacin (99%).

CONCLUSIONS

Our study recommends using local antibiotic sensitivity data for empirical UTI treatment. Amikacin, ertapenem, and meropenem are effective intravenous options. Cephalosporin, cefuroxime, amoxicillin/clavulanic acid, and nitrofurantoin are suitable oral choices. No significant changes in antimicrobial susceptibility were observed during the COVID-19 pandemic. Further research is needed to assess potential pandemic-related alterations.