A new study shows that artificial intelligence (AI) can greatly enhance the accuracy of detecting child physical abuse in emergency departments. Presented at the 2025 Pediatric Academic Societies Meeting, the research highlights how existing coding methods, like ICD-10-CM, often misestimate abuse rates. By using a machine-learning model that analyzes both injury and abuse-specific codes, researchers found more precise predictions. The study examined over 3,300 emergency visits for children under 10, revealing that the AI approach reduced errors significantly compared to traditional methods. This innovation could transform how we understand and respond to child abuse, leading to safer outcomes for vulnerable children.
[ad_2]
[ad_1]
Artificial Intelligence (AI) is making waves in child welfare by improving the accuracy of detecting physical abuse among children. A recent study, set to be presented at the Pediatric Academic Societies (PAS) meeting in Honolulu in April 2025, shows how a machine-learning model outperforms traditional methods that rely on diagnostic codes.
Current coding practices, particularly the use of International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes, fall short in accurately estimating child abuse cases. This study found that relying solely on these codes led to an average misdiagnosis rate of 8.5%. Researchers argue that enhancing coding systems by combining both injury and abuse-specific codes could lead to better estimates of abuse prevalence.
To tackle these shortcomings, the study developed a machine-learning model to analyze diagnostic information from emergency department visits. This model uses both injury-related data and individual abuse cases for more precise predictions. Analyzed data from over 3,300 emergency visits across seven children’s hospitals highlighted the need for accuracy, especially since most children involved were under the age of two.
Key findings indicate that the AI model significantly reduces errors when estimating abuse prevalence. In contrast to traditional methods that often overestimated cases, the AI approach showcased a much narrower error margin. Farah Brink, a child abuse pediatrician involved in the research, pointed out that this technology provides a clearer understanding of child abuse trends, enhancing treatment options and child safety.
This groundbreaking study suggests that AI tools could revolutionize how society understands and deals with sensitive issues like child abuse, thereby leading to better intervention strategies and outcomes for vulnerable children.
Image Credit: © mihakonceptcorn – stock.adobe.com.
[ad_2]
- How does artificial intelligence help in finding child abuse cases?
Artificial intelligence analyzes data from various sources quickly. It can spot patterns that humans might miss, helping identify cases of child abuse more accurately.
- Can AI really make a difference in protecting children?
Yes, AI can help protect children by improving how quickly and accurately cases are reported and handled, which means faster help for those in need.
- What kind of data does AI use to estimate child abuse?
AI uses information from reports, medical records, and even social media to identify potential signs of abuse or neglect.
- Is AI replacing social workers in child protection?
No, AI is not replacing social workers. Instead, it supports them by providing more accurate information, allowing them to focus on helping children and families.
- Are there any risks with using AI in child protection?
Yes, there are risks. If the AI is trained on biased data, it might lead to wrong conclusions. It’s important to use AI carefully and always involve human judgment.
[ad_1]
