Artificial intelligence and machine learning for anaphylaxis algorithms.

Abstract

Purpose of review: Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Current management includes early recognition, prompt administration of epinephrine, and immediate medical attention. However, challenges remain in accurate diagnosis, timely treatment, and personalized care. This article reviews the integration of artificial intelligence and machine learning in enhancing anaphylaxis management.

Recent findings: Artificial intelligence and machine learning can analyze vast datasets to identify patterns and predict anaphylactic episodes, improve diagnostic accuracy through image and biomarker analysis, and personalize treatment plans. Artificial intelligence-powered wearable devices and decision support systems can facilitate real-time monitoring and early intervention. The ethical considerations of artificial intelligence use, including data privacy, transparency, and bias mitigation, are also discussed.

Summary: Future directions include the development of predictive models, enhanced diagnostic tools, and artificial intelligence-driven educational resources. By leveraging artificial intelligence and machine learning, healthcare providers can improve the management of anaphylaxis, ensuring better patient outcomes and advancing personalized medicine.