This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.
Fionda, B., Placidi, E., De Ridder, M., Strigari, L., Patarnello, S., Tanderup, K., Hannoun-Levi, J., Siebert, F., Boldrini, L., Gambacorta, M. A., De Spirito, M., Sala, E., Tagliaferri, L., Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs, <<CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY>>, 2024; 49 (N/A): N/A-N/A. [doi:10.1016/j.ctro.2024.100865] [https://hdl.handle.net/10807/337638]
Artificial intelligence in interventional radiotherapy (brachytherapy): Enhancing patient-centered care and addressing patients’ needs
Fionda, Bruno;Placidi, Elisa;Boldrini, Luca;Gambacorta, Maria Antonietta;De Spirito, Marco;Sala, Evis;Tagliaferri, Luca
2024
Abstract
This review explores the integration of artificial intelligence (AI) in interventional radiotherapy (IRT), emphasizing its potential to streamline workflows and enhance patient care. Through a systematic analysis of 78 relevant papers spanning from 2002 to 2024, we identified significant advancements in contouring, treatment planning, outcome prediction, and quality assurance. AI-driven approaches offer promise in reducing procedural times, personalizing treatments, and improving treatment outcomes for oncological patients. However, challenges such as clinical validation and quality assurance protocols persist. Nonetheless, AI presents a transformative opportunity to optimize IRT and meet evolving patient needs.
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