The article "Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives" by Lorenzo Di Sarno et al. explores the integration of artificial intelligence (AI) in pediatric emergency medicine (PEM). The authors conduct a narrative review structured into two parts: the first part delves into the theoretical principles of AI, providing a background for understanding state-of-the-art tools; the second part analyzes AI models in PEM, focusing on key applications such as triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. These applications have shown superior performance compared to standard methods. However, the widespread adoption of AI in PEM faces challenges, including technological barriers, ethical issues, age-related differences in data interpretation, and the scarcity of comprehensive datasets in the pediatric context. Future research should address the validation of models using prospective datasets with larger sample sizes and tailor AI algorithms to specific medical needs, requiring close collaboration between clinicians and developers. The article also highlights the importance of building shared knowledge platforms to enhance the integration of AI in healthcare.The article "Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives" by Lorenzo Di Sarno et al. explores the integration of artificial intelligence (AI) in pediatric emergency medicine (PEM). The authors conduct a narrative review structured into two parts: the first part delves into the theoretical principles of AI, providing a background for understanding state-of-the-art tools; the second part analyzes AI models in PEM, focusing on key applications such as triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. These applications have shown superior performance compared to standard methods. However, the widespread adoption of AI in PEM faces challenges, including technological barriers, ethical issues, age-related differences in data interpretation, and the scarcity of comprehensive datasets in the pediatric context. Future research should address the validation of models using prospective datasets with larger sample sizes and tailor AI algorithms to specific medical needs, requiring close collaboration between clinicians and developers. The article also highlights the importance of building shared knowledge platforms to enhance the integration of AI in healthcare.