Purpose or Case Report: In recent years, the applications of artificial intelligence in radiology have increased exponentially. Randomized controlled trials demonstrating the potential of AI in radiology have been centered around deep learning techniques with artificial neural networks, such as convolutional neural networks (CNNs). Current applications of CNNs include the detection, segmentation and quantification of pathologic conditions. These tasks use deductive AI, which uses discriminative models to identify discrepancies between images. Generative AI is an emerging technique that has a number of exciting novel applications in the field of Pediatric Radiology. This exhibit will provide a practical overview to familiarize pediatric radiologists with this topic and review current and potential future use cases.

Generative adversarial networks (GAN) require two opposing networks, a generator that synthesizes new images, and a discriminator that differentiates between real and synthesized images. Through an iterative process, GAN creates highly realistic images that have exciting applications in image creation, translation between imaging modalities, acceleration of image synthesis and identification of pathologic abnormalities.

ChatGPT is a popular generative AI model that generates coherent responses to queries, and uses a Generative Pre-trained Transformer (GPT) network to encode and decode language data. ChatGPT is pre-trained on large volume text data to learn how language is used in different contexts. An encoder processes the input sequence and a decoder generates the output sequence. Both encoder and decoder have a multi-head self-attention mechanism allows differential weighing of the sequence to infer meaning and context. Potential applications in pediatric radiology are broad, including patient communication, providing appropriate imaging recommendations, report generation and review of radiologic safety guidelines and regulations. However, generative AI models require careful implementation, training and validation to reduce potential pitfalls in unintentionally synthesizing content that does not exist, or conversely, removing clinically relevant information from an image.

The purpose of this exhibit is to: (1) provide an introduction to Generative AI (GAN) and ChatGPT (2) review use cases of GAN and ChatGPT in pediatric radiology (3) provide practical methods for evaluating the performance of artificial intelligence algorithms in pediatric radiology applications.
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