Ewha Med J.  2024 Oct;47(4):e49. 10.12771/emj.2024.e49.

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

• Jeong C
• Goh Y
• Kwak J

• KMID: 2561405
• DOI: http://doi.org/10.12771/emj.2024.e49

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, 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 CT, MRI, and PET scans to enable precise tumor delineation. In adaptive radiation therapy, 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.

Keyword

Artificial intelligence; Clinical decisionmaking; Computer-assisted radiotherapy planning; Precision medicine; Radiation oncology

Figure

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  Fig. 1. The radiation therapy procedures, tasks performed during the procedures, and subsequent related tasks. The black background signifies the aspects related to opportunities for AI integration, indicating that these AI integrations are influencing the entire radiation therapy process. The tasks within the radiation therapy process that are expected to change or be affected by AI in the near future are presented in black text. AI, artificial intelligence.

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  Fig. 2. The challenges involved in integrating AI into radiation oncology, which are likely to be highly complex and closely interconnected. This indicates the need for careful planning and consideration at an early stage to address these issues before they become entangled. AI, artificial intelligence.

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  Fig. 3. Advancements anticipated to result from the integration of AI into radiation therapy. These significant advancements are expected to lead to enhanced diagnostic accuracy, seamless patient monitoring, real-time therapeutic adaptations, and the delivery of highly personalized treatment, thereby substantially improving outcomes for many cancer patients. AI, artificial intelligence.

Reference

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