Importance: There is variability in practice and imaging usage to diagnose cervical spine injury (CSI) following blunt trauma in pediatric patients.
Objective: To develop a prediction model to guide imaging usage and to identify trends in imaging and to evaluate the PEDSPINE model.
Design, Setting, And Participants: This cohort study included pediatric patients (<3 years years) following blunt trauma between January 2007 and July 2017.
Artificial intelligence (AI) systems hold great promise to improve healthcare over the next decades. Specifically, AI systems leveraging multiple data sources and input modalities are poised to become a viable method to deliver more accurate results and deployable pipelines across a wide range of applications. In this work, we propose and evaluate a unified Holistic AI in Medicine (HAIM) framework to facilitate the generation and testing of AI systems that leverage multimodal inputs.
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