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.

Non-coding RNAs as key players in the neurodegenerative diseases: Multi-platform strategies and approaches for exploring the Genome's dark matter.

A growing amount of evidence in the last few years has begun to unravel that non-coding RNAs have a myriad of functions in gene regulation. Intensive investigation on non-coding RNAs (ncRNAs) has led to exploring their broad role in neurodegenerative diseases (NDs) owing to their regulatory role in gene expression. RNA sequencing technologies and transcriptome analysis has unveiled significant dysregulation of ncRNAs attributed to their biogenesis, upregulation, downregulation, aberrant epigenetic regulation, and abnormal transcription. Despite these advances, the understanding of their potential as therapeutic targets and biomarkers underpinning detailed mechanisms is still unknown. Advancements in bioinformatics and molecular technologies have improved our knowledge of the dark matter of the genome in terms of recognition and functional validation. This review aims to shed light on ncRNAs biogenesis, function, and potential role in NDs. Further deepening of their role is provided through a focus on the most recent platforms, experimental approaches, and computational analysis to investigate ncRNAs. Furthermore, this review summarizes and evaluates well-studied miRNAs, lncRNAs and circRNAs concerning their potential role in pathogenesis and use as biomarkers in NDs. Finally, a perspective on the main challenges and novel methods for the future and broad therapeutic use of ncRNAs is offered.

Recall of Prior Knowledge in Medical Microbiology Among Medical Interns: A Multicenter Cross-Sectional Assessment in Saudi Arabia.

Background: Retention of basic biomedical sciences knowledge is of great importance in medical practice. This study aimed to provide some insights into medical interns' ability to recall theoretical knowledge of medical microbiology and to explore factors that affect its retention. Methods: In this cross-sectional study conducted between January and March 2019, an anonymized questionnaire with 10 validated multiple-choice questions about medical microbiology was distributed as hard copies to test the ability to recall knowledge of Saudi medical interns in three tertiary training hospitals in Riyadh, Saudi Arabia. Results: A total of 300 medical interns [164 females (54.7%) and 136 males (45.3%)], in three major tertiary medical care centers in Riyadh, Saudi Arabia, voluntarily participated in the study. Almost a third of participants, 107 (36.4%), graduated from medical schools adopting a traditional curriculum, whereas 184 (63.6%) graduated from medical schools adopting problem-based learning (PBL) instructional approach. The overall mean score out of 10 marks was 3.9±1.8 with almost 82% failures scoring less than six marks. Both total and pass/fail grades were significantly associated with interns who graduated from private colleges. Scores were not significantly associated with any of the investigated parameters except type of college (governmental vs private) with a p-value of 0.049. Conclusion: The current study revealed an overall poor recall of knowledge in microbiology among interns. Our findings suggest a need for a careful revision of curriculum to correct deficiencies, particularly in teaching medical microbiology. Integration of basic sciences is required as well as aligning teaching of basic medical sciences with clinical skills.