Artificial Intelligence in Pediatric Emergency Medicine: Applications, Challenges, and Future Perspectives

• Published in: Biomedicines Vol. 12; no. 6; p. 1220
• Main Authors: Di Sarno, Lorenzo, Caroselli, Anya, Tonin, Giovanna, Graglia, Benedetta, Pansini, Valeria, Causio, Francesco Andrea, Gatto, Antonio, Chiaretti, Antonio
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 30.05.2024; MDPI
• Subjects: Age; Algorithms; Artificial intelligence; Big Data; Blockchain; Brain; Cognition & reasoning; Computational linguistics; Data analysis; Datasets; Decision making; deep learning; Emergency medical care; Emergency medicine; Information processing; Injuries; Innovations; Language processing; Machine learning; Medical innovations; Medical personnel; Medicine; Multimedia; Natural language; Natural language interfaces; Patients; Pediatric emergencies; pediatric emergency medicine; Pediatrics; Prediction models; Review; Sepsis; Technology application; Traumatic brain injury; deep learning; pediatric emergency medicine; machine learning; artificial intelligence; pediatrics
• ISSN: 2227-9059; 2227-9059
• DOI: 10.3390/biomedicines12061220

The dawn of Artificial intelligence (AI) in healthcare stands as a milestone in medical innovation. Different medical fields are heavily involved, and pediatric emergency medicine is no exception. We conducted a narrative review structured in two parts. The first part explores the theoretical principles of AI, providing all the necessary background to feel confident with these new state-of-the-art tools. The second part presents an informative analysis of AI models in pediatric emergencies. We examined PubMed and Cochrane Library from inception up to April 2024. Key applications include triage optimization, predictive models for traumatic brain injury assessment, and computerized sepsis prediction systems. In each of these domains, AI models outperformed standard methods. The main barriers to a widespread adoption include technological challenges, but also ethical issues, age-related differences in data interpretation, and the paucity of comprehensive datasets in the pediatric context. Future feasible research directions should address the validation of models through prospective datasets with more numerous sample sizes of patients. Furthermore, our analysis shows that it is essential to tailor AI algorithms to specific medical needs. This requires a close partnership between clinicians and developers. Building a shared knowledge platform is therefore a key step.