AI-Based Solutions for Improving Radiation Dose Distribution in Oncology

Abstract

One important part of radiation treatment in cancer is making sure that the radiation dose reaches the tumours as evenly as possible while hurting as few healthy tissues as possible. Even though traditional methods work, they don't always get the doses to the right places, which could limit the treatment. New developments in artificial intelligence (AI) hold promise for improving the accuracy of how radiation doses are distributed in cancer. AI-based methods, especially deep learning and machine learning, could completely change the way treatment plans are made by allowing automated and accurate dose prediction models, making it easier to target tumours accurately, and making the best use of radiation delivery. Computer programs that are driven by AI can look at complicated medical images like computed tomography (CT) scans and magnetic resonance imaging (MRI) to make very accurate dose distributions and find important structures that need to be protected. AI can also help with adaptive radiation therapy by constantly changing treatment plans as the tumour size or the patient's body changes during the treatment. This makes sure that the treatment is more personalized and optimized on the fly. By learning from big datasets to guess the best radiation tactics, machine learning models, especially convolutional neural networks (CNNs) and reinforcement learning, can make treatments even more effective. This essay looks at different AI techniques used to spread radiation doses in cancer. It also talks about how these techniques affect the accuracy of treatment and the problems that might come up when AI methods are used in real life. When AI is added to radiation treatment, it is expected to greatly improve patient results, lower side effects, and make personalized cancer care possible.

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