Applications of Monte Carlo Simulations in Medical Physics: Enhancing Precision and Accuracy in Radiation Therapy

<p dir="ltr">Monte Carlo (MC) simulations are the backbone of medical physics, especially radiation therapy, due to their high accuracy in modeling the interaction of ionizing radiation with biological tissues. These significantly improve the accuracy of dose calculations and thus are of primary importance for treatment planning and quality assurance. Still, a high computational cost remains one of the substantial limiting factors for their wider use in clinical applications.</p><p dir="ltr">This paper investigates the advantages of MC-based dose calculations compared to conventional algorithms; it underlines their superior precision and computational complexity. A comparative evaluation of MC techniques and deep learning-based methodologies is carried out in terms of performance with respect to accuracy, computational efficiency, and robustness in heterogeneous media. Results are presented that show Monte Carlo simulations reach up to 99.2 % accuracy but are computationally intensive, requiring substantial optimization through AI-driven acceleration techniques. Recent developments, such as GPU acceleration and hybrid AI-MC approaches, have shown great promise to close the gap between accuracy and computational feasibility.</p><p dir="ltr">In addition, this study has demonstrated how AI-based variance reduction techniques contribute to the optimization of MC simulations by gaining execution time with no loss of their intrinsic accuracy. Future research efforts should focus on the development and integration of deep learning models in MC simulations to further advance the efficiency of MC simulations. These results of this study illustrated that hybrid AI-MC frameworks may achieve advances in computational efficiency while maintaining reliability for clinical use and thus can make Monte Carlo simulations more feasible in real-time medical applications.</p>