Development of AI-Powered Radiation Therapy Planning Systems for Personalized Treatment - Abbas, Reyam Qaiser; Sadiq, Muhammad Ali Haitham; Kazim, Hussein Hani; Abdullah, Omar Saber Rzayyig; ragi, Zainab Ali (American Journal of Botany and Bioengineering, 2025)
Radiation therapy is a key cancer treatment modality, yet traditional treatment planning remains labor-intensive, time-consuming, and prone to variability. The integration of artificial intelligence (AI) in radiation therapy planning offers a transformative approach to optimizing treatment strategies. This study explores the application of AI-powered systems, including machine learning and deep learning algorithms, to automate treatment planning, enhance precision, and personalize dose distribution. Using AI-driven predictive models, treatment plans are optimized for patient-specific anatomical variations while reducing human-induced errors and improving efficiency. Findings indicate that AI-powered planning significantly improves treatment accuracy, reduces planning time, and enhances clinical workflow. The results highlight the potential of AI in radiation oncology to improve patient outcomes and streamline radiation therapy processes, emphasizing the need for further integration into clinical practice.