Revolutionizing Radiation Oncology Through Artificial Intelligence

Artificial Intelligence (AI) is making headlines across the world today. It is helping drive up the stock prices of many companies, inspiring startups, and creating excitement about how it will improve lives. In this article, we review how AI can be used to revolutionize radiation oncology, which relies heavily on computer software and digital data to treat cancer patients using radiation therapy (RT). First, we describe AI and how it functions. Second, we reviewed how AI can have transformative applications in radiation oncology to improve accuracy, efficiency, and precision across (1) treatment planning, (2) segmentation, (3) motion management, (4) quality assurance, (5) personalized treatment, (6) treatment response, and (7) tumor position. Finally, we conclude by describing potential solutions for challenges around data, education, regulation, and security across the rapidly advancing landscape of AI in radiation oncology.

Authors

Publication Details

Journal
Journal of Student Research
Published
2024-11-30
DOI
https://doi.org/10.47611/jsrhs.v13i4.8288
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00
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article

Revolutionizing Radiation Oncology Through Artificial Intelligence

Sakinah Khuram
Journal of Student Research
Radiomics and Machine Learning in Medical Imaging
article

Revolutionizing Radiation Oncology Through Artificial Intelligence

Sakinah Khuram
article en

Abstract

Artificial Intelligence (AI) is making headlines across the world today. It is helping drive up the stock prices of many companies, inspiring startups, and creating excitement about how it will improve lives. In this article, we review how AI can be used to revolutionize radiation oncology, which relies heavily on computer software and digital data to treat cancer patients using radiation therapy (RT). First, we describe AI and how it functions. Second, we reviewed how AI can have transformative applications in radiation oncology to improve accuracy, efficiency, and precision across (1) treatment planning, (2) segmentation, (3) motion management, (4) quality assurance, (5) personalized treatment, (6) treatment response, and (7) tumor position. Finally, we conclude by describing potential solutions for challenges around data, education, regulation, and security across the rapidly advancing landscape of AI in radiation oncology.

Journal of Student ResearchVol. 13(4)
Openalex Percentile: Top 55%
Radiomics and Machine Learning in Medical Imaging
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