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.

Anxiety, Depression, and Common Chronic Diseases, and Their Association With Social Determinants in Saudi Primary Care.

INTRODUCTION: Patients with chronic diseases can experience psychological conditions, including anxiety and depression. However, the association between chronic diseases and these psychological conditions remains unclear. This study aimed to identify the relationship between anxiety, depression, and common chronic diseases (hypertension, type 2 diabetes, dyslipidemia, and rheumatoid arthritis), and their association with social determinants at an outpatient primary care setting. METHODS: The validated hospital anxiety and depression scale was administered electronically to eligible participants. For each condition (anxiety and depression), participants were categorized as normal, borderline abnormal, and abnormal, according to their score out of 21 (≤7 = normal, 8-10 = borderline abnormal, ≥11 = abnormal). The scores and numbers of participants in each category were analyzed and compared with their demographic characteristics and chronic diseases for associations and relationships. RESULTS: We recruited 271 participants (mean age of 51.65 + 11.71 years) attending primary care clinics. Of these patients, 17.7% and 8.9% had borderline abnormal and abnormal depression, respectively, and 10.3% and 8.9% of patients had borderline abnormal anxiety and abnormal anxiety. Common social determinants and lifestyle factors were examined. Age, gender, and sugary drinks' consumption significantly increased the odds of hypertension and type 2 diabetes; vigorous physical activity 3 times a week, decreased the odds of developing these chronic diseases. Adjusted regression models showed a statistically significant association between the hospital anxiety and depression scale score for borderline and abnormal anxiety and the presence of type 2 diabetes (OR 3.04 [95% CI 1.13, 8.19], P-value = .03 and OR 4.65 [95% CI 1.63,13.22], P-value <.03, respectively) and dyslipidemia (OR 5.93 [95% CI 1.54, 22.86], P-value = .01, and OR 4.70 [95% CI 0.78, 28.35], P-value = .09, respectively). The odds of developing depression were 4 times higher (P-value .04) in patients with rheumatoid arthritis. CONCLUSION: Among patients attending primary care outpatient clinics, anxiety, and depression were significantly associated with type 2 diabetes and rheumatoid arthritis, respectively. Social determinants and lifestyle factors play a major role in the development of common chronic diseases in Saudi Arabia. Primary care physicians should consider the patients' psychological status, sociodemographic status, and lifestyle risks during the management of chronic diseases.