James S. Barry

Abstract AI education in medicine remains fragmented, narrowly technical, and disconnected from healthcare realities. Through an international collaboration, we developed a framework spanning seven domains and 24 learning objectives. We reflect on what this process revealed: effective AI education must be clinically grounded, ethically integrated, and implementation-aware across the training continuum.

Ask a clinical question and tap into Healio AI's knowledge base. Key takeaways: James Barry, MD, MBA, led a discussion on AI in pediatrics in a Healio Community webinar. Clara Lin, MD, and Ryan McAdams, MD, agreed that AI innovation in pediatrics is lagging behind adult medicine. There is a gap in AI innovation between pediatric and adult medicine, with pediatrics lagging behind, experts said.

Artificial intelligence (AI) research in pediatric medicine is expanding rapidly, but only a small fraction of models reach clinical use, exposing a critical gap in real-world implementation, evaluation, and reporting. A systematic review by Schouten et al. found that out of 262 pediatric AI studies, only one model (~0.38%) reached a technology readiness level of 8, meaning the model underwent clinical testing.