TY - JOUR
T1 - Artificial intelligence (AI) and interventional radiotherapy (brachytherapy): State of art and future perspectives
AU - Fionda, Bruno
AU - Boldrini, Luca
AU - D’Aviero, Andrea
AU - Lancellotta, Valentina
AU - Gambacorta, Maria Antonietta
AU - Kovacs, Gyorgy
AU - Patarnello, Stefano
AU - Valentini, Vincenzo
AU - Tagliaferri, Luca
PY - 2020
Y1 - 2020
N2 - Purpose: Artificial intelligence (AI) plays a central role in building decision supporting systems (DSS), and its application in healthcare is rapidly increasing. The aim of this study was to define the role of AI in healthcare, with main focus on radiation oncology (RO) and interventional radiotherapy (IRT, brachytherapy). Artificial intelligence in interventional radiation therapy: AI in RO has a large impact in providing clinical decision support, data mining and advanced imaging analysis, automating repetitive tasks, optimizing time, and modelling patients and physicians’ behaviors in heterogeneous contexts. Implementing AI and automation in RO and IRT can successfully facilitate all the steps of treatment workflow, such as patient consultation, target volume delineation, treatment planning, and treatment delivery. Conclusions: AI may contribute to improve clinical outcomes through the application of predictive models and DSS optimization. This approach could lead to reducing time-consuming repetitive tasks, healthcare costs, and improving treatment quality assurance and patient’s assistance in IRT.
AB - Purpose: Artificial intelligence (AI) plays a central role in building decision supporting systems (DSS), and its application in healthcare is rapidly increasing. The aim of this study was to define the role of AI in healthcare, with main focus on radiation oncology (RO) and interventional radiotherapy (IRT, brachytherapy). Artificial intelligence in interventional radiation therapy: AI in RO has a large impact in providing clinical decision support, data mining and advanced imaging analysis, automating repetitive tasks, optimizing time, and modelling patients and physicians’ behaviors in heterogeneous contexts. Implementing AI and automation in RO and IRT can successfully facilitate all the steps of treatment workflow, such as patient consultation, target volume delineation, treatment planning, and treatment delivery. Conclusions: AI may contribute to improve clinical outcomes through the application of predictive models and DSS optimization. This approach could lead to reducing time-consuming repetitive tasks, healthcare costs, and improving treatment quality assurance and patient’s assistance in IRT.
KW - Artificial intelligence
KW - Brachytherapy
KW - Decision supporting system
KW - Interventional radiotherapy
KW - Machine learning
KW - Personalized medicine
KW - Artificial intelligence
KW - Brachytherapy
KW - Decision supporting system
KW - Interventional radiotherapy
KW - Machine learning
KW - Personalized medicine
UR - http://hdl.handle.net/10807/198602
U2 - 10.5114/jcb.2020.100384
DO - 10.5114/jcb.2020.100384
M3 - Article
SN - 1689-832X
VL - 12
SP - 497
EP - 500
JO - Journal of Contemporary Brachytherapy
JF - Journal of Contemporary Brachytherapy
ER -