Application and progress of artificial intelligence in radiation therapy dose prediction

Area of Science:

• Medical Physics
• Oncology
• Radiotherapy Technology

Background:

• Radiation therapy (RT) is a cornerstone of cancer treatment, demanding precise dose distribution for therapeutic efficacy.
• Current dose prediction methods are time-consuming, labor-intensive, and prone to inconsistencies due to variations in expertise and institutional practices.
• Artificial intelligence (AI) has emerged as a transformative technology in various medical fields, including RT.

Purpose of the Study:

• To review the advancements in AI for radiation therapy dose prediction.
• To highlight the specific contributions and progress of deep learning (DL) in this domain.
• To provide an overview of AI's impact on improving the accuracy and efficiency of RT dose planning.

Main Methods:

• Literature review of AI applications in radiation therapy.
• Focus on research utilizing deep learning (DL) algorithms for dose prediction.
• Analysis of studies demonstrating the clinical implementation and benefits of AI in RT.

Main Results:

• AI, particularly DL, has shown significant promise in automating and enhancing RT dose prediction.
• AI-driven dose prediction offers improved consistency and accuracy compared to traditional methods.
• Several AI-based products have been successfully implemented in clinical practice, confirming their superiority.

Conclusions:

• AI, especially DL, is a rapidly advancing field with substantial potential to optimize radiation therapy.
• The integration of AI in dose prediction can lead to more efficient and effective cancer treatments.
• Continued research and development in AI are crucial for realizing the full benefits of precision medicine in oncology.