| Abstrakt: |
A study conducted at Kwangwoon University in Seoul, South Korea, focused on enhancing the accuracy of artificial intelligence models for Pressure Injury assessment using real and synthetic datasets. The researchers found that combining real and synthetic data significantly improved model accuracy, suggesting that synthetic datasets can effectively address limitations of small-scale datasets in medical applications. The study highlights the potential of using diverse synthetic data generation methods to enhance the generalization and robustness of AI models for Pressure Injury assessment. [Extracted from the article] |
