Some Things Change, Some Things Stay the Same: Trends in Canadian Education in Paediatric Cardiology and the Cardiac Sciences

Warren, Andrew E.; Tham, Edythe B; Abeysekera, Jayani

doi:10.1016/j.cjcpc.2022.08.004

Cited by 3 publications

(1 citation statement)

References 48 publications

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“…Cardiac 3D models are one option in their toolkit that can help with sharing information, engaging patients and families, strengthening relationships with families, and promoting greater involvement in healthcare through shared decision-making. 16 …”

Section: Utility In Cardiology Educationmentioning

confidence: 99%

3D Printed Models in Cardiology Training

Chetan,

Valverde,

Yoo

2024

JACC: Advances

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Section: Utility In Cardiology Educationmentioning

confidence: 99%

3D Printed Models in Cardiology Training

Chetan,

Valverde,

Yoo

2024

JACC: Advances

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

Temsah,

Alhuzaimi,

Almansour

et al. 2024

J Med Syst

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Arti cial Intelligence (AI), particularly AI-Generated Imagery, holds the capability to transform 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. The study involved generating a total of 110 images for normal human heart and 20 common CHDs through DALL•E 3. Then, 33 healthcare professionals systematically assessed these AI-generated images by variable levels of healthcare professionals (HCPs) using a developed framework to individually assess each image anatomical accuracy, in-picture text usefulness, image appeal to medical professionals and the potential to use the image 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 signi cantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were signi cant challenges identi ed in image generation. These ndings suggest adopting a cautious approach in integrating AI imagery in medical education, emphasizing the need for rigorous validation and interdisciplinary collaboration. The study advocates for future AI-models to be ne-tuned with accurate medical data, enhancing their reliability and educational utility.

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Artificial intelligence in cardiology: the trainee perspective

Young

2024

Intelligence-Based Cardiology and Cardiac Surgery

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Some Things Change, Some Things Stay the Same: Trends in Canadian Education in Paediatric Cardiology and the Cardiac Sciences

Cited by 3 publications

References 48 publications

3D Printed Models in Cardiology Training

3D Printed Models in Cardiology Training

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 in cardiology: the trainee perspective

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