Adaptive AI Algorithms for Real-Time Monitoring and Intervention in Pediatric Healthcare Systems

Abstract

Implementing AI into pediatric health care has the potential to revolutionize patient care by providing accurate, individualized treatment plans. This paper discusses the step-by-step process of implementing an integrated AI system to meet the personalized care needs of children in the healthcare setting. The use of smart rules is made possible through the application of adaptive algorithms Longest Common Subsequence (LCS), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), Decision Tree and more. In this way, we plan to measure the extent to which the integrated AI system will assist in enhancing the diagnostic accuracy and management plans of patients when applied to different cases. Furthermore, the study explores the compatibility of the system with current EHR systems and healthcare information technology environments to ascertain its real-world implementation in the current clinical settings. The findings of this research should show that integrating the proposed hybrid AI system in real-time monitoring can help identify appropriate information for healthcare decision making and thus increase the quality of health care for children. The current study aims to address some of these gaps by presenting a systematic approach of implementing AI in pediatric care, which can help progress the field of healthcare by incorporating cutting-edge technologies for enhanced decision making.