ISCAP Proceedings: Abstract Presentation
ASPIRE: AI-Supported Platform for Innovative Radiology Education
Jennifer Schulte
Dakota State University
Mark Spanier
Dakota State University
Abstract
Radiologists complete approximately thirteen years of rigorous education and training before certification. That is thirteen years in which medical technologies and workflows continue to evolve. Artificial Intelligence (AI) has emerged as one of the most transformative changes already assisting radiologists with image classification and report generation. Yet, many practitioners enter the field with limited exposure to AI, and skepticism persists due to burnout, unfamiliarity, and the “black box” nature of current systems. To address these challenges, we propose ASPIRE, an educational platform built on MAIRA-2: Grounded Radiology Report Generation (2024). ASPIRE integrates explainable AI (XAI) features and interactive learning elements to improve trust and usability. By exposing students to AI tools during their training and education, the platform aims to reduce future learning curves and foster confidence in AI-assisted workflows. While still in development, ASPIRE has the potential to become a valuable supplement to radiology curricula. It can bridge education and emerging technologies, improve technical understanding, and foster long-term acceptance of AI as a partner in medical practice.