AccScience Publishing / ARNM / Online First / DOI: 10.36922/arnm.3523
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PERSPECTIVE ARTICLE

Optimizing conventional radiotherapy: A synergistic approach with generative artificial intelligence and computational sustainability

João Melo e Castro1* ,  José Neves1,2
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1 Artificial Intelligence and Health Research Unit, Polytechnic Health Higher Institute of the North/Advanced Polytechnic and University Cooperative, Famalicão, Portugal
2 Department of information and Technology, University of Minho, Braga, Minho & Artificial Intelligence and Health Research Unit, Polytechnic Health Higher Institute of the North/ Advanced Polytechnic and University Cooperative, Famalicão, Portugal
ARNM 2024, 2(2), 3523 https://doi.org/10.36922/arnm.3523
Submitted: 29 April 2024 | Accepted: 13 June 2024 | Published: 25 June 2024
© 2024 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution 4.0 International License ( https://creativecommons.org/licenses/by/4.0/ )
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Abstract

Conventional radiotherapy (CR) stands at a critical juncture, poised for transformation through the integration of cutting-edge technologies. This article explores the transformative potential of integrating generative artificial intelligence (GAI) and computational sustainability (CS) principles into CR. The convergence of GAI techniques, such as generative adversarial networks, with CS offers novel approaches for optimizing treatment planning, enhancing precision, and ensuring long-term sustainability in radiotherapy practices. We delve deeper into the personalized medicine strategy facilitated by generative models, taking into account patient-specific anatomical variations and dose optimization. The article highlights the role of GAI in adaptive radiotherapy, enabling real-time adjustments to treatment plans based on dynamic changes in patient anatomy. CS principles contribute to resource optimization and energy efficiency, addressing the environmental impact of CR practices. The synergy between GAI and CS fosters innovations in treatment techniques, data-driven decision-making, and ethical considerations, promoting equitable access and minimizing disparities. This article provides a comprehensive overview of the potential benefits and challenges associated with the integration of GAI and CS in CR, shaping the future of precision, efficiency, and sustainable radiotherapy practices.

Keywords
Generative artificial intelligence
Computational sustainability
Conventional radiotherapy
Treatment planning optimization
Adaptive radiotherapy
Funding
None.
Conflict of interest
The authors declare that they have no competing interests.
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