The Evolving Role of Artificial Intelligence in Radiation Therapy: From Clinical Applications to Future Perspectives

Rahul Rai; Krishna Kumar Singh

doi:10.55524/ijirem.2026.13.4.7

Abstract

Artificial intelligence (AI) is rapidly changing many medical specialties, and Radiation Therapy is one of them. This paper deals with the adoption of AI in Radiation Therapy, its benefits and its challenges. Artificial intelligence can improve the accuracy, speed, and results of radiation therapy and may also aid in adaptive radiation therapy, predictive analytics, optimized treatment planning, and improved image analysis. Artificial intelligence can help cut planning times and boost accuracy by analyzing large data sets to determine the best treatment settings and automate difficult tasks. AI-driven methods are increasingly improving tumor detection and segmentation in image analysis by analyzing data from CT, MRI, and PET imaging, leading to precise delineation of the tumor. Adaptive radiation therapy uses AI to improve the accuracy and efficacy of treatment by adjusting treatment regimens in real time as a patient’s anatomy and tumor growth change. Using previous patient data for predictive analytics in order to predict treatment outcomes and potential complications make clinical decision making and more personalized treatment strategies possible. Barriers to AI implementation in radiotherapy include: - Data quality and quantity - Interoperability and standardization - Ethical and regulatory issues - Resistance to clinical implementation to meet these challenges, researchers, clinicians, data scientists, and business stakeholders must collaborate. AI could help overcome these barriers to advance radiation treatment, better patient care and streamline operations. This study provides an overview of the state of the art of the integration of AI in radiation therapy and recommendations for future lines of research and clinical application.

Keywords

Radiation Therapy, Artificial Intelligence (AI), Radiation Oncology, Adaptive Radiation Therapy (ART)

References

T. M. Ahmed, S. Kawamoto, R. H. Hruban, E. K. Fishman, P. Soyer, and L. C. Chu, “A primer on artificial intelligence in pancreatic imaging,” Diagn. Interv. Imaging, vol. 104, no. 9, pp. 435–447, Sep. 2023. Available from: https://doi.org/10.1016/j.diii.2023.03.002
E. Huynh et al., “Artificial intelligence in radiation oncology,” Nat. Rev. Clin. Oncol., vol. 17, no. 12, pp. 771–781, Dec. 2020. Available from: https://doi.org/10.1038/s41571-020-0417-8
Wang, X. Zhu, J. C. Hong, and D. Zheng, “Artificial intelligence in radiotherapy treatment planning: Present and future,” Technol. Cancer Res. Treat., vol. 18, p. 1533033819873922, Jan. 2019. Available from: https://doi.org/10.1177/1533033819873922
Y. Fu et al., “Artificial intelligence in radiation therapy,” IEEE Trans. Radiat. Plasma Med. Sci., vol. 6, no. 2, pp. 158–181, Feb. 2022. Available from: https://doi.org/10.1109/TRPMS.2021.3107454
R. Rattan et al., “Artificial intelligence in oncology, its scope and future prospects with specific reference to radiation oncology,” BJR Open, vol. 1, no. 1, p. 20180031, 2019. Available from: https://doi.org/10.1259/bjro.20180031
L. Ma et al., “Correlation between AI-based CT organ features and normal lung dose in adjuvant radiotherapy following breast-conserving surgery: A multicenter prospective study,” BMC Cancer, vol. 23, no. 1, p. 1085, Nov. 2023. Available from: https://doi.org/10.1186/s12885-023-11554-2
L. Huang et al., “MRI-based radiomics models for the early prediction of radiation-induced temporal lobe injury in nasopharyngeal carcinoma,” Front. Neurol., vol. 14, p. 1135978, 2023. Available from: https://doi.org/10.3389/fneur.2023.1135978
M. Rabe, C. Kurz, A. Thummerer, and G. Landry, “Artificial intelligence for treatment delivery: Image-guided radiotherapy,” Strahlenther. Onkol., vol. 201, no. 3, pp. 283–297, Mar. 2025. Available from: https://doi.org/10.1007/s00066-024-02277-9
S. Psoroulas, A. Paunoiu, S. Corradini, J. Hörner-Rieber, and S. Tanadini-Lang, “MR-linac: Role of artificial intelligence and automation,” Strahlenther. Onkol., vol. 201, no. 3, pp. 298–305, Mar. 2025. Available from: https://doi.org/10.1007/s00066-024-02358-9
R. Jensen, H. M. Nellemann, and J. Overgaard, “Tumor progression in waiting time for radiotherapy in head and neck cancer,” Radiother. Oncol., vol. 84, no. 1, pp. 5–10, Jul. 2007. Available from: https://doi.org/10.1016/j.radonc.2007.04.001
L. E. Court, “The Radiation Planning Assistant: Addressing the global gap in radiotherapy services,” Lancet Oncol., vol. 25, no. 3, pp. 277–278, Mar. 2024. Available from: https://doi.org/10.1016/S1470-2045(24)00084-6
F. Putz et al., “The Segment Anything foundation model achieves favorable brain tumor auto-segmentation accuracy in MRI to support radiotherapy treatment planning,” Strahlenther. Onkol., vol. 201, no. 3, pp. 255–265, Mar. 2025. Available from: https://doi.org/10.1007/s00066-024-02313-8
N. T. C. Fung, W. M. Hung, C. K. Sze, M. C. H. Lee, and W. T. Ng, “Automatic segmentation for adaptive planning in nasopharyngeal carcinoma IMRT: Time, geometrical, and dosimetric analysis,” Med. Dosim., vol. 45, no. 1, pp. 60–65. Available from: https://doi.org/10.1016/j.meddos.2019.06.002
Y. Rong et al., “NRG Oncology assessment of artificial intelligence deep learning-based auto-segmentation for radiation therapy: Current developments, clinical considerations, and future directions,” Int. J. Radiat. Oncol. Biol. Phys., vol. 119, no. 1, pp. 261–280, May 2024. Available from: https://doi.org/10.1016/j.ijrobp.2023.10.033
X. Liu, L. Qu, Z. Xie, J. Zhao, Y. Shi, and Z. Song, “Towards more precise automatic analysis: A systematic review of deep learning-based multi-organ segmentation,” Biomed. Eng. Online, vol. 23, no. 1, p. 52, Jun. 2024. Available from: https://doi.org/10.1186/s12938-024-01238-8
Y. Zi, Q. Wang, Z. Gao, X. Cheng, and T. Mei, “Research on the application of deep learning in medical image segmentation and 3D reconstruction,” Acad. J. Sci. Technol., vol. 10, no. 2, pp. 8–12, Apr. 2024. Available from: https://doi.org/10.54097/0h77ge77
Santhakumar, D. G. Takale, S. Tyagi, R. Anitha, M. Tiwari, and J. A. Dhanraj, “Analysis of multimodality fusion of medical image segmentation employing deep learning,” in Human Cancer Diagnosis and Detection Using Exascale Computing, Wiley, 2024, pp. 171–183. Available from: https://doi.org/10.1002/9781394197705.ch11
Lim Joon et al., “Prostate cancer post-prostatectomy radiotherapy: CT vs MRI for vesico-urethral anastomosis target delineation,” Radiother. Oncol., vol. 125, no. 1, pp. 113–117, Oct. 2017. Available from: https://doi.org/10.1016/j.radonc.2017.08.031
G. Aldosary et al., “Geometric inaccuracy and co-registration errors for CT, DynaCT and MRI images used in robotic stereotactic radiosurgery treatment planning,” Phys. Medica, vol. 69, pp. 212–222, Jan. 2020. Available from: https://doi.org/10.1016/j.ejmp.2019.12.002
M. Boulanger et al., “Deep learning methods to generate synthetic CT from MRI in radiotherapy: A literature review,” Phys. Med., vol. 89, pp. 265–281, Sep. 2021. Available from: https://doi.org/10.1016/j.ejmp.2021.07.027
Y. Peng et al., “Magnetic resonance-based synthetic computed tomography images generated using generative adversarial networks for nasopharyngeal carcinoma radiotherapy treatment planning,” Radiother. Oncol., vol. 150, pp. 217–224, Sep. 2020. Available from: https://doi.org/10.1016/j.radonc.2020.06.049
L. Schwartz and L. Dong, “Adaptive radiation therapy for head and neck cancer—Can an old goal evolve into a new standard?” J. Oncol., vol. 2011, 2011. Available from: https://doi.org/10.1155/2011/690595
K. Glide-Hurst et al., “Adaptive radiation therapy (ART) strategies and technical considerations: A state of the ART review from NRG Oncology,” Int. J. Radiat. Oncol. Biol. Phys., vol. 109, no. 4, pp. 1054–1075, Mar. 2021. Available from: https://doi.org/10.1016/j.ijrobp.2020.10.021
Liu et al., “Review of cone beam computed tomography based online adaptive radiotherapy: Current trend and future direction,” Radiat. Oncol., vol. 18, no. 1, p. 144, Sep. 2023. Available from: https://doi.org/10.1186/s13014-023-02340-2
Y.-F. Wang et al., “Enhancing safety in AI-driven cone beam CT-based online adaptive radiation therapy: Development and implementation of an interdisciplinary workflow,” Adv. Radiat. Oncol., vol. 9, no. 3, p. 101399, Mar. 2024. Available from: https://doi.org/10.1016/j.adro.2023.101399
S. E. Whang, Y. Roh, H. Song, and J.-G. Lee, “Data collection and quality challenges in deep learning: A data-centric AI perspective,” VLDB J., vol. 32, no. 4, pp. 791–813, Jul. 2023. Available from: https://doi.org/10.1007/s00778-022-00775-9
B. Murdoch, “Privacy and artificial intelligence: Challenges for protecting health information in a new era,” BMC Med. Ethics, vol. 22, no. 1, p. 122, Dec. 2021. Available from: https://doi.org/10.1186/s12910-021-00687-3
M. Bukowski et al., “Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: Are we ready from an international perspective?” Eur. Radiol., vol. 30, no. 10, pp. 5510–5524, Oct. 2020. Available from: https://doi.org/10.1007/s00330-020-06874-x
M. Kawamura et al., “Revolutionizing radiation therapy: The role of AI in clinical practice,” J. Radiat. Res., vol. 65, no. 1, pp. 1–9, Jan. 2024. Available from: https://doi.org/10.1093/jrr/rrad090
A. Wahid et al., “Artificial intelligence for radiation oncology applications using public datasets,” Semin. Radiat. Oncol., vol. 32, no. 4, pp. 400–414, Oct. 2022. Available from: https://doi.org/10.1016/j.semradonc.2022.06.009

Cites this article as

R. Rai, K. K. Singh, "The Evolving Role of Artificial Intelligence in Radiation Therapy: From Clinical Applications to Future Perspectives", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-13, Issue-4, Page No-50-57, 2026. Available from: https://doi.org/10.55524/ijirem.2026.13.4.7