Personalized decision-making through AI solutions in pediatric emergency medicine: Focusing on febrile children

Abstract

Pediatric emergency medicine (PEM) presents unique challenges due to the diverse developmental stages and medical conditions of young patients. The increasing patient load and nonurgent referrals to pediatric emergency departments (PEDs) emphasize the need for personalized decision-making approaches. These approaches must accommodate the complexities of pediatric care while fostering collaboration between healthcare providers and families. Integrating artificial intelligence (AI) into healthcare settings can transform PEM by enhancing diagnostic accuracy, customizing treatments, and optimizing resource allocation. AI technologies leverage vast datasets, including electronic health records and genetic profiles, to generate personalized diagnostic and treatment plans. Machine learning algorithms can identify patterns in complex data, facilitating early disease detection and precise interventions. This literature review analyzes the role of AI in supporting pediatric emergency care through diagnostic assistance, predictive modeling for febrile disease progression, and outcome optimization. It also highlights the challenges of applying AI in PEM, including data limitations and the need for algorithmic transparency. By addressing these challenges, AI has the potential to revolutionize personalized care in pediatric emergency settings, ultimately improving patient outcomes and care delivery.

Ähnliche Arbeiten