Author ORCID Identifier

0000-0001-6825-6393

Document Type

Conference Paper

Disciplines

Statistics

Publication Details

Statistical and Machine Learning: Methods and Applications (SAML-25) on June 5th and 6th, 2025 at TU Dublin, Ireland.

doi:10.21427/er8w-xc04

Abstract

The planning of radiation oncology treatment is made more dynamic and individualized by Artificial Intelligence (AI). Routine radiotherapy practice applies normative procedures indifferent to patient-specific parameters such as tumor volume, patient anatomy, and heterogeneity in the delineation of treatment response. Inadequate and over-radiation treatment is the most prevalent outcome. Further, with the inclusion of AI, it can facilitate enhancing the healthcare industry through optimizing radiotherapy using an array of patient information such as molecular profiles and imaging data. The product offers an end-to-end AI-driven solution to all aspects of radiotherapy, from initial consultation (diagnosis) to adaptive treatment planning. All the sub-system parts like tumour and organ segmentation using Convolutional Neural Networks (CNNs), radiosensitivity estimation using machine learning algorithms, and real-time dose adaptation tools are part of the framework to achieve accuracy-efficiency trade-off in optimized radiotherapy. Moreover, aside from the technical, this book focuses on ethics and social aspects like data privacy, GDPR compliance, and explainability using interpretability methods with saliency maps. Comparative analysis demonstrates improvements in target accuracy, planning time, and patient-specific adjustability over conventional methods. Finally, the research aims to bridge the gap between high technology innovation and ethically sound clinical application, promoting more efficient and equitable cancer treatment. The findings demonstrate that AI-aided planning significantly enhances precision while reducing man-hours, with a clear path for secure and scalable implementation in the clinic.

DOI

https://doi.org/10.21427/er8w-xc04

Creative Commons License

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.

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