Alkhulaifat Dana,  Rafful Patricia,  Lopez Rippe Julian,  Khalkhali Vahid,  Welsh Michael,  Wieczkowski Sydney,  Reid Janet,  Sotardi Susan

Final Pr. ID: Poster #: EDU-043

In recent years, artificial intelligence (AI) applications for radiology have undergone exponential growth. AI has the potential to assist pediatric radiologists in several respects, including identification of various pathologies and prioritization of studies with urgent imaging findings. Although image analysis is an important application of AI, there are many additional use cases where AI can assist radiologists, including radiology operations and quality analysis. Therefore, it is critical that radiologists familiarize themselves with the wide range of AI methodologies and applications.
Although efforts have been made to create formal education for radiologists, currently available resources typically rely upon didactic lectures, without real-life context for the technology. However, case-based education is a principal component of radiology education. Furthermore, prior research has demonstrated that trainees and radiologists are interested in case-based approaches for learning AI [1].

Thus, the purpose of this educational exhibit is to provide an informative resource specifically for pediatric radiologists which utilizes a case-based learning approach. This resource uses 5 cases intended to demonstrate the variety of uses of AI in pediatric radiology, while illustrating the underlying process of AI model selection and development. Specifically, these cases will be selected to encompass AI topics including: image classification, lesion segmentation, natural language processing for imaging reports, recommender systems to enhance radiologist practice and predictive modeling for radiology operations research. This approach allows us to explore decision-points in AI model building, while examining potential pitfalls that may arise. This educational exhibit was developed based on a thorough literature review of prior AI research combined with our professional experience, and aims to provide educational resource intended to teach AI model selection and development to pediatric radiologists.

References
1. Velez-Florez MC, Ghosh A, Patton D, et al (2022) Artificial intelligence curriculum needs assessment for a pediatric radiology fellowship program: what, how, and why? Acad Radiol. https://doi.org/10.1016/j.acra.2022.04.026 Read More

Authors:  Alkhulaifat Dana , Rafful Patricia , Lopez Rippe Julian , Khalkhali Vahid , Welsh Michael , Wieczkowski Sydney , Reid Janet , Sotardi Susan

Keywords:  Artificial Intelligence, Case-Based Learning, Education