Automation and artificial intelligence in radiation therapy treatment planning

Autor: Scott Jones, Kenton Thompson, Brian Porter, Meegan Shepherd, Daniel Sapkaroski, Alexandra Grimshaw, Catriona Hargrave
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: Journal of Medical Radiation Sciences, Vol 71, Iss 2, Pp 290-298 (2024)
Druh dokumentu: article
ISSN: 2051-3909
2051-3895
DOI: 10.1002/jmrs.729
Popis: Abstract Automation and artificial intelligence (AI) is already possible for many radiation therapy planning and treatment processes with the aim of improving workflows and increasing efficiency in radiation oncology departments. Currently, AI technology is advancing at an exponential rate, as are its applications in radiation oncology. This commentary highlights the way AI has begun to impact radiation therapy treatment planning and looks ahead to potential future developments in this space. Historically, radiation therapist's (RT's) role has evolved alongside the adoption of new technology. In Australia, RTs have key clinical roles in both planning and treatment delivery and have been integral in the implementation of automated solutions for both areas. They will need to continue to be informed, to adapt and to transform with AI technologies implemented into clinical practice in radiation oncology departments. RTs will play an important role in how AI‐based automation is implemented into practice in Australia, ensuring its application can truly enable personalised and higher‐quality treatment for patients. To inform and optimise utilisation of AI, research should not only focus on clinical outcomes but also AI's impact on professional roles, responsibilities and service delivery. Increased efficiencies in the radiation therapy workflow and workforce need to maintain safe improvements in practice and should not come at the cost of creativity, innovation, oversight and safety.
Databáze: Directory of Open Access Journals
Externí odkaz:
Nepřihlášeným uživatelům se plný text nezobrazuje