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Advances in Radiotherapy
& Nuclear Medicine Generative AI enhances radiotherapy

scientists, engineers, and policymakers. By working doi: 10.7150/ijms.3635
together, stakeholders can overcome technical, clinical, 3. Chen X, Du J, Huang J, Zeng Y, Yuan K. Neoadjuvant and
regulatory, and ethical hurdles, ensuring the successful adjuvant therapy in intrahepatic cholangiocarcinoma. J Clin
integration of AI and CS into CR practices. Ultimately, this Transl Hepatol. 2022;10(3):553-563.
transformative intersection promises not only to refine doi: 10.14218/JCTH.2021.00250
treatment outcomes but also to redefine the landscape of
CR for a more sustainable and patient-centered future. 4. Koka K, Verma A, Dwarakanath BS, Papineni RVL.
Technological advancements in external beam radiation
This study was limited by the novelty of the field, with therapy (EBRT): An indispensable tool for cancer treatment.
limited existing studies on the actual use and application Cancer Manag Res. 2022;14:1421-1429.
of AI in CR, and how CS can contribute to minimizing the doi: 10.2147/CMAR.S351744
environmental impact of CR. Thus, further exploration
of the topics described in this study through additional 5. Chargari C, Deutsch E, Blanchard P, et al. Brachytherapy: An
investigation is necessary. This exploration should delve overview for clinicians. CA Cancer J Clin. 2019;69(5):386-401.
into both the benefits and challenges of applying these doi: 10.3322/caac.21578.
emerging technologies to CR, translating findings into 6. Bubeck S, Chandrasekaran V, Eldan R, et al. Sparks of
practical clinical guidelines for CR. Artificial General Intelligence: Early Experiments with
GPT-4. ArXiv; 2023;5.
Acknowledgments
doi: 10.48550/arXiv.2303.12712
None.
7. Lehmann F, Buschek D. Examining autocompletion as a
Funding basic concept for interaction with generative AI. I-Com.
2020;19(3):251-264.
None.
doi: 10.1515/icom-2020-0025
Conflict of interest 8. Castelvecchi D. Can we open the black box of AI? Nature.
2016;538(7623):20-23.
The authors declare that they have no competing interests.
doi: 10.1038/538020a
Author contributions 9. Brynjolfsson E, Mitchell T. What can machine learning do?
Conceptualization: João Melo e Castro Workforce implications. Science. 2017;358(6370):1530-1534.
Writing – original draft: João Melo e Castro doi: 10.1126/science.aap8062
Writing – review & editing: José Neves
10. Janiesch C, Zschech P, Heinrich K. Machine learning and
Ethics approval and consent to participate deep learning. Electron Mark. 2021;31(3):685-695.
doi: 10.1007/s12525-021-00475-2
Not applicable.
11. Kühl N, Schemmer M, Goutier M, Satzger G. Artificial
Consent for publication intelligence and machine learning. Electron Mark.
2022;32(4):2235-2244.
Not applicable.
doi: 10.1007/s12525-022-00598-0
Availability of data 12. Samtani S, Zhu H, Padmanabhan B, Chai Y, Chen H,
Not applicable. Nunamaker F Jr. Deep learning for information systems
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