AI Enhances Detection of Child Abuse Cases

**AI Model Improves Identification of Physical Child Abuse in Emergency Rooms**

A recent study has revealed that artificial intelligence (AI) can more effectively identify cases of physical child abuse in emergency room settings compared to traditional diagnostic coding methods. Researchers designed a machine-learning model that demonstrated enhanced accuracy in estimating the prevalence of abuse by analyzing high-risk injuries and specific indicators associated with physical abuse.

The study focused on developing an AI-driven approach to address limitations in existing diagnostic systems, which often rely on manual coding processes. By incorporating data related to injury patterns and other abuse-related factors, the machine-learning model provided a more precise assessment of potential abuse cases. The findings suggest that AI could play a critical role in improving detection rates and supporting healthcare professionals in identifying vulnerable children who may require intervention.

Further details about the study are available at Neuroscience News.

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Source: https://neurosciencenews.com/ai-child-abuse-detection-28741/

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Author

Mark Chiang

紐約 NYU Computer Science 碩士 紐澤西 UMDNJ Biomedical Informatics 博士 中研院博士後 台灣大學博士後