(37) "Exploring the Future of Pediatric Oncology: A Literature Review on Precision Medicine and Artificial Intelligence Innovations”

Title: "Exploring the Future of Pediatric Oncology: A Literature Review on Precision Medicine and Artificial Intelligence Innovations”

Abstract : Basis of Inquiry:
The integration of artificial intelligence (AI) and machine learning (ML) in pediatric oncology is rapidly evolving, offering potential improvements in diagnostics, treatment strategies, and personalized care. However, the implications for pediatric oncology nursing require further exploration.

Purpose:
AI and ML are transforming oncology by analyzing large datasets, including genomic, clinical, and imaging data, to optimize diagnosis, treatment, and patient outcomes. In pediatric oncology, where treatment must be tailored to each patient’s genetic and developmental profile, AI has the potential to enhance care by identifying biomarkers, predicting treatment responses, and personalizing therapies. AI-driven imaging tools are also improving early cancer detection, which is critical for pediatric patients. However, challenges such as data privacy, algorithm transparency, and clinical validation remain. Understanding AI’s current applications and ethical concerns is essential for its successful integration into pediatric oncology practice.

Methods:
A comprehensive literature review was conducted using databases such as PubMed, ScienceDirect, and Frontiers in Oncology. Search terms included "Pediatric AND Precision Medicine AND Oncology," "Artificial Intelligence AND Pediatrics OR Oncology," and "Precision Medicine AND Artificial Intelligence AND Pediatric Oncology." Studies published in the last five years focusing on AI’s role in pediatric oncology and precision medicine were prioritized.
Findings:
AI and ML are advancing precision medicine in pediatric oncology by improving diagnosis, treatment planning, and response prediction. These innovations enhance cancer detection and treatment optimization. However, challenges such as data privacy, transparency, and clinical validation persist. Ethical concerns, including parental trust in AI-driven decisions, must also be addressed. Despite these challenges, AI holds significant potential to improve outcomes and reduce risks for children with cancer.