Review

Challenges and opportunities to integrate artificial intelligence in radiation oncology: a narrative review

Chiyoung Jeong1, YoungMoon Goh1, Jungwon Kwak1,*
Author Information & Copyright
1Asan Medical Center, Seoul 05505, Korea.
*Corresponding Author: Jungwon Kwak, Asan Medical Center, Seoul 05505, Korea, Republic of. E-mail: jwkwak0301@gmail.com.

© Copyright 2024 Ewha Womans University School of Medicine. This is an Open-Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Received: Jul 31, 2024; Revised: Aug 31, 2024; Accepted: Sep 02, 2024

Published Online: Oct 31, 2024

Abstract

Artificial intelligence (AI) is rapidly transforming various medical fields, including radiation oncology. This review explores the integration of AI into radiation oncology, highlighting both challenges and opportunities. AI can improve the precision, efficiency, and outcomes of radiation therapy by optimizing treatment planning, enhancing image analysis, facilitating adaptive radiation therapy (ART), and enabling predictive analytics. Through the analysis of large datasets to identify optimal treatment parameters, AI can automate complex tasks, reduce planning time, and improve accuracy. In image analysis, AI-driven techniques enhance tumor detection and segmentation by processing data from computed tomography, magnetic resonance imaging, and positron emission tomography scans to enable precise tumor delineation. In ART, AI is beneficial because it allows real-time adjustments to treatment plans based on changes in patient anatomy and tumor size, thereby improving treatment accuracy and effectiveness. Predictive analytics using historical patient data can predict treatment outcomes and potential complications, guiding clinical decision-making and enabling more personalized treatment strategies. Challenges to AI adoption in radiation oncology include ensuring data quality and quantity, achieving interoperability and standardization, addressing regulatory and ethical considerations, and overcoming resistance to clinical implementation. Collaboration among researchers, clinicians, data scientists, and industry stakeholders is crucial to overcoming these obstacles. By addressing these challenges, AI can drive advancements in radiation therapy, improving patient care and operational efficiencies. This review presents an overview of the current state of AI integration in radiation oncology and insights into future directions for research and clinical practice.

Keywords: Artificial intelligence; Clinical decision-making; Computer-assisted radiotherapy planning; Precision medicine; Radiation oncology