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Recognition of Genetic Conditions After Learning With Images Created Using Generative Artificial Intelligence
Rebekah L Waikel1, Amna A Othman1, Tanviben Patel1
1Medical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland.
JAMA Network Open
|March 15, 2024
Summary
Real and AI-generated images improved pediatric residents' ability to recognize Kabuki syndrome (KS) and Noonan syndrome (NS). Real images were most effective, but AI images also showed promise for rare genetic conditions.
Area of Science:
- Medical Genetics
- Medical Education
- Artificial Intelligence in Medicine
Background:
- Pediatric residency programs face challenges in genetics education due to a lack of standardization and a shortage of medical geneticists.
- Innovative educational tools are needed to enhance residents' diagnostic skills for rare genetic syndromes.
Purpose of the Study:
- To compare the effectiveness of different educational interventions in improving pediatric residents' recognition of Kabuki syndrome (KS) and Noonan syndrome (NS).
- To evaluate the utility of generative artificial intelligence (AI) in creating educational images for rare genetic conditions.
Main Methods:
- A comparative effectiveness study involving US pediatric residents.
- Generative AI was used to create images of children with KS and NS.
- Residents were surveyed after viewing images from four interventions: text-only descriptions, real images, and two types of AI-generated images.
Main Results:
- Both real and AI-generated images significantly improved the recognition of KS and NS compared to text-only descriptions.
- Real images demonstrated the highest sensitivity for diagnosing KS and NS.
- AI-generated images were non-inferior to real images and significantly reduced resident uncertainty in diagnosing these conditions.
Conclusions:
- Visual aids, including both real and AI-generated images, are valuable tools for teaching rare genetic syndromes to pediatric residents.
- Generative AI offers a promising adjunctive educational resource, particularly for conditions with limited real-world image availability.
- Further integration of AI in medical education can help bridge gaps in specialized knowledge and improve diagnostic accuracy.
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