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REVIEW

Natural Language Processing for Radiation Oncology: Personalizing Treatment Pathways

, , & ORCID Icon
Pages 65-76 | Received 23 Aug 2023, Accepted 29 Jan 2024, Published online: 12 Feb 2024

Figures & data

Figure 1 A schematic overview of the flow from foundational data to diverse applications in the radiation oncology domain empowered by NLP methods. The bottom layer represents various foundational data sources used in radiation oncology. The middle layer categorizes the predominant NLP methodologies into three classes: knowledge-based, statistical, and deep learning, where knowledge-based methods rely on domain-specific rules, statistical methods employ algorithms to infer patterns from data, and deep learning utilizes complex neural network architectures for more nuanced language understanding. The top layer displays the key applications of these NLP methods in radiation oncology.

Figure 1 A schematic overview of the flow from foundational data to diverse applications in the radiation oncology domain empowered by NLP methods. The bottom layer represents various foundational data sources used in radiation oncology. The middle layer categorizes the predominant NLP methodologies into three classes: knowledge-based, statistical, and deep learning, where knowledge-based methods rely on domain-specific rules, statistical methods employ algorithms to infer patterns from data, and deep learning utilizes complex neural network architectures for more nuanced language understanding. The top layer displays the key applications of these NLP methods in radiation oncology.