Cautiously optimistic: A survey of radiation oncology professionals’ perceptions of automation in radiotherapy planning

Batumalai, Vikneswary; Jameson, Michael; King, Odette; Walker, Rhiannon; Slater, C.; Dundas, Kylie; Dinsdale, G.; Wallis, Andrew; Ochoa, Cesar E.; Gray, R A; Vial, P; Vinod, Shalini

doi:10.1016/j.tipsro.2020.10.003

Cited by 12 publications

(25 citation statements)

References 20 publications

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“…AI has the potential to improve efficiency in service delivery, provide consistent quality of care and equity in access through standardising clinical practice across New Zealand [30]. This is consistent with findings from Australia where researchers found the majority of respondents felt automation will increase consistency in planning, work output and productivity, and quality of planning [4]. The efficiency gains can be channelled to tasks that require human interventions and emotions such as plan checking and longer review clinics.…”

Section: Discussionsupporting

confidence: 68%

“…Only a minority (11%Þ of respondents are using auto segmentation tools only but over 60% are using both auto segmentation and manual tools. A similar study in Australia reported 45% of respondents used somewhat automated methods to contour organs at risk [4]. The relatively low usage of automated tools may suggest that current tools are not sufficiently developed to meet the minimum standards and need manual modification.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, data scientists have little to no clinical knowledge, and are limited in knowledge and understanding when needing to identify important clinical parameters for consideration in algorithm use and deployment [31]. In a survey of Australian radiation therapy profession, it was reported that 66% of respondents thought automation will change the primary tasks of certain jobs [4]. To develop a sustainable workforce driven by and competent with AI and ML processes, the core professional groups; radiation oncologists, medical physicists and radiation therapists in a tripartite forum, must work with universities and radiation oncology administrators to explore the creation of a new professional role within radiation oncology.…”

Section: Discussionmentioning

confidence: 99%

“…In the future, supported by the use of AI, the foundation of work within radiotherapy will move from a focus on routine tasks, to tackling more complex and creative work, requiring critical thinking and collaborative efforts [3]. Several AI applications are emerging across the radiation oncology workflow [4] all aimed at benefitting patients through addressing such issues as improved standards of care, fewer side effects and an enhanced quality of life. Automation and AI brings with it the advantages of improved efficiency, standardised patient care and the potential to develop decision support systems.…”

Section: Introductionmentioning

confidence: 99%

“…In New Zealand, emerging technologies like AI have not yet had any significant effects on the labour markets [6]. Studies to evalu- ate the perceptions of radiation oncology professionals to AI have been done in other jurisdictions with results indicating a cautious embrace of the new technology [4,7]. AI and ML are set to transform the practice of radiation oncology [8], and there is an opportunity for radiation oncologists, radiation therapists and medical physicists to play a prominent role in shaping a future dominated by the emerging technology [9].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: A New Zealand study

Mugabe

2021

Technical Innovations & Patient Support in Radiation Oncolo

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Section: Discussionsupporting

confidence: 68%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: A New Zealand study

Mugabe

2021

Technical Innovations & Patient Support in Radiation Oncolo

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Radiation therapist perceptions on how artificial intelligence may affect their role and practice

O'Shaughnessey

Collins

2022

J of Medical Radiation Sci

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Introduction:The use of artificial intelligence (AI) has increased in medical radiation science, with advanced computing and modelling. Considering radiation therapists (RTs) perceptions of how this may affect their role is imperative, as this will contribute to increasing the efficiency of implementation and improve service delivery. Methods: A peer-reviewed anonymous survey was developed and completed by 105 RTs between April and June 2021. The online survey was distributed via the Medical Radiation Practice Board of Australia and the Australian Society of Medical Imaging and Radiation Therapy newsletter as well as professional networks. The survey gained perceptions of the impact of AI on radiation therapy practice and RTs roles within Australia, and data were analysed using quantitative data analysis and thematic analysis. Results: Automation is used throughout radiation therapy practice, with 68% of RTs being optimistic about this. The majority (63%) had little to no knowledge of working with AI and 96% would like to learn more including the underpinnings of AI and its safe and ethical use. Many (66%) perceived AI would affect their role, including increasing their skillset and reducing mundane tasks, whereas others (23%) perceived it would reduce job satisfaction by increasing repetition and limiting their problem-solving ability. AI was perceived to impact the patient positively (67%), increasing efficiency and accuracy of radiotherapy treatments; however, it could depersonalise patient care. Conclusion: RTs perceive embracing AI in radiotherapy has the potential to advance the profession and improve the service to patients. If AI is implemented with sufficient training for greater understanding, and management uses these benefits to improve patient care, rather than replace RTs roles, then overall any negatives will be outweighed by the benefits.

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Automation and artificial intelligence in radiation therapy treatment planning

Jones,

Thompson,

Porter

et al. 2023

J of Medical Radiation Sci

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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.

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Cautiously optimistic: A survey of radiation oncology professionals’ perceptions of automation in radiotherapy planning

Cited by 12 publications

References 20 publications

Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: A New Zealand study

Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: A New Zealand study

Radiation therapist perceptions on how artificial intelligence may affect their role and practice

Automation and artificial intelligence in radiation therapy treatment planning

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