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.

Cost Consequence Analysis of Belimumab versus Standard of Care for the Management of Systemic Lupus Erythematosus in Saudi Arabia: A Retrospective Cohort Study.

BACKGROUND: Belimumab use for the management of systemic lupus erythematosus (SLE) has been limited, in part due to its high acquisition cost relative to the standard of care (SoC) and the uncertainties about its cost-effectiveness. Therefore, the aim of this study was to compare the cost and effectiveness of belimumab versus the SoC alone for the management of SLE using real-world data from the perspective of public healthcare payers in Saudi Arabia. METHODS: Data were retrieved from a national prospective cohort of SLE, Saudi Arabia. Adult SLE patients (≥18 yrs.) treated with belimumab plus the SoC or the SoC alone for at least six months were recruited. The effectiveness was measured using the Systemic Lupus Erythematosus Disease Activity Index 2000 (SLEDAI-2K). Unit costs for health services and prescription drugs were retrieved from the Saudi ministry of health. Nonparametric bootstrapping with inverse probability weighting was conducted to generate the 95% confidence limits for the cost and effectiveness. RESULTS: A total of 15 patients on belimumab plus the SoC and 41 patients on the SoC alone met the inclusion criteria and were included in the analysis. The majority of patients were females (91.07%) with a mean age of 38 years. The mean difference in cost and SLEDAI-2K score reduction between belimumab versus the SoC were USD 5303.16 [95% CI: USD 2735.61-USD 7802.52] and 3.378 [95% CI: 1.769-6.831], respectively. Belimumab demonstrated better effectiveness but higher cost in 96% of the bootstrap cost-effectiveness distributions. CONCLUSION: Future studies should use more robust research designs and a larger sample size to confirm the findings of this study.