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Advances in Radiotherapy
            & Nuclear Medicine                                                      Artificial intelligence in radiotherapy



              Despite the ethical and legal dilemmas surrounding   4.   Huynh E, Hosny A, Guthier C, et al. Artificial intelligence in
            AI usage, present advancements suggest that AI will   radiation oncology. Nat Rev Clin Oncol. 2020;17(12):771-781.
            coexist with existing systems or even replace some of      doi: 10.1038/s41571-020-0417-8
            them. Furthermore, in the face of such extensive big data,
            not utilizing AI may be both scientifically and ethically   5.   Yakar M, Etiz D, Metintas M, Ak G, Celik O. Prediction
                                                                  of radiation pneumonitis with machine learning in stage
            questionable.
                                                                  III lung cancer: A  pilot study.  Technol Cancer Res Treat.
              The success of AI systems depends on large, accurate,   2021;20:15330338211016373.
            and diverse datasets. Such datasets can help prevent      doi: 10.1177/15330338211016373
            algorithmic biases and enhance generalizability. To achieve
            this, internationally standardized and secured datasets   6.   Akcay  M,  Etiz  D, Celik  O, Ozen  A.  Evaluation of  acute
                                                                  hematological toxicity by machine learning in gynecologic
            should be established, accompanied with measures to   cancers using postoperative radiotherapy. Indian J Cancer.
            ensure data privacy.
                                                                  2022;59(2):178-186.
              For AI algorithms to transition into clinical practice,      doi: 10.4103/ijc.IJC_666_19
            multicenter validation studies must be conducted. It is
            essential to  demonstrate  that model performance yields   7.   Akcay M, Etiz D, Celik O, Ozen A. Evaluation of prognosis
            consistent results across diverse patient populations. In   in nasopharyngeal cancer using machine learning. Technol
                                                                  Cancer Res Treat. 2020;19:1533033820909829.
            addition, to enable the effective and accurate use of AI,
            training programs should be organized for clinicians and      doi: 10.1177/1533033820909829
            professionals working in healthcare technologies.  8.   Akcay M, Etiz D, Celik O. Prediction of survival and
              Ethical and legal guidelines must be established    recurrence patterns by machine learning in gastric cancer
            internationally to define AI’s role in clinical decision-  cases undergoing radiation therapy and chemotherapy. Adv
            making. These guidelines should delineate AI’s boundaries   Radiat Oncol. 2020;5(6):1179-1187.
            as a tool and ensure that these technologies operate under      doi: 10.1016/j.adro.2020.07.007
            human oversight while prioritizing ethical principles, such   9.   Janssen S, El Shafie RA, Ruder AM, et al. Mobile applications
            as informed patient consent.                          in radiation oncology-current choices and future potentials.
              Although the use of AI in radiation oncology is still in   Strahlenther Onkol. 2023;199(4):337-349.
            its early stages, implementing the above recommendations      doi: 10.1007/s00066-023-02048-y
            will bring these technologies one step closer to a reliable,   10.  Bibault JE, Giraud P, Burgun A. Big Data and machine
            effective, and widespread clinical application. This progress   learning in radiation oncology: State of the art and future
            has the potential to transform healthcare by improving   prospects. Cancer Lett. 2016;382(1):110-117.
            patient outcomes and increasing the efficiency of medical
            services.                                             doi: 10.1016/j.canlet.2016.05.033
                                                               11.  Bibault JE, Giraud P. Deep learning for automated
            Conflict of interest                                  segmentation in radiotherapy: A  narrative review.  Br J
                                                                  Radiol. 2024;97(1153):13-20.
            The author declares that she has no conflict of interest and
            has no competing interests.                           doi: 10.1093/bjr/tqad018
                                                               12.  Samarasinghe G, Jameson M, Vinod S, et al. Deep learning
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            Volume 3 Issue 2 (2025)                        100                             doi: 10.36922/arnm.8429
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