Artificial intelligence: a new ally for diagnosis in pediatric emergency care

By Elodie Vaz | Published on March 19, 2026 | 3 min read


Elbow injuries are a common reason for consultation in pediatric emergency departments. However, their radiological diagnosis remains challenging. In children, the skeleton is still developing and includes numerous growth plates that are invisible or poorly visible on X-rays. In addition, some fractures can be extremely subtle.  

In this context, interpreting elbow radiographs is a real clinical challenge, even for experienced practitioners. A missed fracture can lead to inappropriate management and, ultimately, functional complications. Faced with this diagnostic difficulty, teams from the pediatric emergency department and the radiology department at Nantes University Hospital explored an innovative approach: integrating an artificial intelligence (AI) algorithm as a decision-support tool.  

Assessing the contribution of AI in fracture detection  

The objective of the study conducted at Nantes University Hospital was to measure the impact of a deep learning algorithm on clinicians’ diagnostic performance in detecting elbow fractures in children.  

The researchers retrospectively analyzed radiographs from 755 children aged 0 to 15 years who were treated in the pediatric emergency department for elbow trauma between January 2019 and April 2020. The goal was to determine whether the use of an AI system could improve diagnostic sensitivity and reduce the risk of missed fractures in an emergency setting.  

A comparative methodology  

To establish a reference diagnosis, two independent experts reviewed all radiological examinations without knowledge of the algorithm’s results.  

Diagnoses made by emergency physicians were then compared across three configurations: without technological assistance, with theoretical assistance from the AI algorithm, and with the algorithm used alone. This comparative approach aimed to precisely quantify the contribution of artificial intelligence to clinical practice.  

Significantly improved diagnostic performance  

The results, published last January in the European Journal of Radiology, show a significant impact of AI on fracture detection. Algorithm-assisted interpretation achieved a diagnostic sensitivity of 99%, reflecting near-complete detection of fractures when clinicians were supported by the tool.  

Moreover, the study demonstrated a gain in sensitivity of over 20% for emergency physicians when assisted by AI. This improvement directly translates into a reduced risk of missed fractures, a major issue in pediatric trauma care.  

According to Dr. Fleur Lorton, pediatrician in the pediatric emergency department at Nantes University Hospital and author of the study, AI should be viewed as a complement to medical expertise:  

“Interpreting elbow radiographs in children is a particularly demanding task in pediatric trauma care. Artificial intelligence does not replace the physician; it acts as a second reader—a co-pilot that enhances our analysis. Through this human–machine collaboration, we reduce the risk of error and improve the quality of care for young patients,” she explained in a press release from Nantes University Hospital published on March 9.  

Toward broader integration of AI in pediatric emergency medicine  

This work, resulting from collaboration between pediatric emergency, radiology, and the Women–Children–Adolescents Clinical Investigation Center at Nantes University Hospital, highlights the potential of AI tools in medical imaging.  

Following this study, an AI-based software dedicated to trauma care is now used in routine practice in the hospital’s pediatric emergency department. It can automatically identify and localize various abnormalities on limb radiographs, including fractures, dislocations, and joint effusions.  

A new study is also underway to evaluate an algorithm applied to chest radiographs, with the aim of extending these tools to acute respiratory conditions, which are particularly common in pediatrics.  

AI as a “second clinical reader”  

The Nantes study confirms the potential of artificial intelligence as a decision-support tool in emergency settings. By enhancing clinicians’ diagnostic sensitivity, these systems can help secure radiological interpretation and improve the management of young patients.  

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About the Author – Elodie Vaz
Health journalist, CFPJ graduate (2023).
Élodie explores the marks diseases leave on bodies and, more broadly, on human life. A registered nurse since 2010, she spent twelve years at patients’ bedsides before exchanging her stethoscope for a notebook. She now investigates the links between environment and health, convinced that the vitality of life cannot be reduced to that of humans alone.