Artificial intelligence and data-driven automation tools have many applications that aim at impacting treatment quality in many domains of radiotherapy, including improving treatment efficiency, reducing patient toxicity, understanding patient outcomes, and improving treatment consistency. By developing and implementing data-based strategies, these tools have the potential to vastly improve patient care within radiotherapy. Comprehensive clinical implementation is critical for the quality gains to be realized.
Foundational concepts and model building are prevalent within the radiation medicine community, but comprehensive clinical implementation remains limited. It is important to emphasize and encourage research and translational successes. Methodologies and frameworks can be adopted to demonstrate implementation strategies to enable broad impact on radiotherapy treatment quality. Artificial intelligence and data-driven automation each have a unique role in supporting and informing patient care and unique barriers to adoption in the clinic. All disease sites will be accepted for review but with an emphasis on topics pertaining to gynecologic and breast cancer.
The Research Topic will emphasize original work with clinical applications in artificial intelligence and automation that have a demonstrable impact on radiotherapy treatment quality, with a particular focus on applications to breast cancer and gynecological cancer. These articles may focus on clinical decision support, treatment plan quality, treatment efficiency, and improving patient outcomes.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
Artificial intelligence and data-driven automation tools have many applications that aim at impacting treatment quality in many domains of radiotherapy, including improving treatment efficiency, reducing patient toxicity, understanding patient outcomes, and improving treatment consistency. By developing and implementing data-based strategies, these tools have the potential to vastly improve patient care within radiotherapy. Comprehensive clinical implementation is critical for the quality gains to be realized.
Foundational concepts and model building are prevalent within the radiation medicine community, but comprehensive clinical implementation remains limited. It is important to emphasize and encourage research and translational successes. Methodologies and frameworks can be adopted to demonstrate implementation strategies to enable broad impact on radiotherapy treatment quality. Artificial intelligence and data-driven automation each have a unique role in supporting and informing patient care and unique barriers to adoption in the clinic. All disease sites will be accepted for review but with an emphasis on topics pertaining to gynecologic and breast cancer.
The Research Topic will emphasize original work with clinical applications in artificial intelligence and automation that have a demonstrable impact on radiotherapy treatment quality, with a particular focus on applications to breast cancer and gynecological cancer. These articles may focus on clinical decision support, treatment plan quality, treatment efficiency, and improving patient outcomes.
Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted as part of this Research Topic.
