# AI in Radiation Oncology: A Comprehensive Review of Current Applications and Future Directions

## TL;DR

This paper reviews how AI is currently used in radiation oncology and explores its potential to improve treatment planning and delivery.

## Contribution

The paper provides a comprehensive review of AI applications in radiation oncology and highlights future directions for integrating AI into clinical workflows.

## Key findings

- AI improves efficiency and accuracy in radiation treatment planning and delivery.
- Deep learning enhances segmentation and dose calculation without sacrificing quality.
- AI can predict treatment-related toxicities using clinical and image data.

## Abstract

At its core, radiation oncology uses knowledge and expertise from multiple precise disciplines such as physics, mathematics, and computer science, which converge with biology and medicine. This is why the rapidly developing AI use in medicine has immense potential in radiotherapy at different levels, such as image reconstruction, volumetric segmentation, radiotherapy delivery, and treatment response. In this review, we aim to provide a summary of current AI use in radiation oncology, mapping in which areas these tools have already been incorporated, as well as their contributions to radiotherapy workflow. Here, we analyze how machine learning software increases the efficiency and accuracy of radiation treatment planning, delivery, and outcome prediction, providing a comprehensive picture of the advancements, limitations, and future directions of AI use in radiotherapy. The radiotherapy workflow consists of multiple intensive steps that are crucial to planning individualized treatment. The introduction of AI assures quality and standardization and reduces variability and time spent in processes such as image reconstruction, segmentation, and dose calculation. Deep learning segmentation reduces planning and delivery time without sacrificing quality. AI predictive capabilities enable clinicians to anticipate and reduce treatment-related toxicities through accuracy based on clinical parameters and image data. Building powerful models requires extensive and robust high-quality data that maintains privacy and HIPAA compliance and must be collected with precision and accuracy. This process, however, can present ethical and logistical obstacles, such as clinical validation needs and reproducibility standards that must be addressed to fully integrate AI into clinical workflows alongside human oversight.