Enhanced Child Abuse Detection Through Artificial Intelligence, Study Shows

Recent advancements in artificial intelligence (AI) have demonstrated significant potential in improving the identification of child abuse cases, especially those presenting in emergency rooms. A new study presented at the Pediatric Academic Societies (PAS) 2025 Meeting in Honolulu highlighted that AI models can more accurately estimate instances of physical child abuse compared to traditional methods relying solely on diagnostic codes. The research analyzed data from over 3,300 injury and abuse-related emergency visits at seven pediatric hospitals from February 2021 to December 2022, focusing on children under ten, with nearly 75% being under two years old.

The study employed a machine-learning approach that considers diagnostic codes associated with high-risk injuries and physical abuse. This method outperformed standard diagnostic coding, which commonly misdiagnoses approximately 8.5% of abuse cases, leading to underreporting or misclassification. According to Dr. Farah Brink, a child abuse pediatrician at Nationwide Children's Hospital, this AI-powered approach provides a clearer understanding of abuse trends, enabling healthcare providers to deliver more appropriate interventions and improve child safety.

The integration of AI tools in clinical settings represents a promising shift towards more precise detection and understanding of child abuse. Such technological innovations can assist medical professionals in making better-informed decisions, ultimately enhancing outcomes for vulnerable children.

For further details, refer to the study: A machine learning approach to improve estimation of physical abuse. Additional information can be found through the American Pediatric Society.