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. 2025 Jan 5:33:100300.
doi: 10.1016/j.tipsro.2025.100300. eCollection 2025 Mar.

Radiation therapists' perspectives on artificial intelligence: Insights from a single institution on Improving effectiveness and educational supports

Caroline Marr  1 , Yat Tsang  1   2
Affiliations

Radiation therapists' perspectives on artificial intelligence: Insights from a single institution on Improving effectiveness and educational supports

Caroline Marr et al. Tech Innov Patient Support Radiat Oncol. .

Abstract

Introduction: In recent years, artificial intelligence (AI) technology has played an evolving role in radiation science, influencing the clinical practice of radiation therapists. This study aimed to explore the knowledge, attitude, clinical applications, and learning needs from the perspective of radiation therapists.

Materials and methods: This study used a cross-sectional online survey with a population of radiation therapists from a single institution. The survey was developed iteratively and was based on past literature. The questions were constructed to measure perception using four themes: knowledge of AI, perceived utilization, job impact, clinical applications, learning needs, and educational support. The data was analyzed using descriptive statistics according to the key themes.

Results: Between 22nd December 2023 and 17th January 2024, 74 radiation therapists completed the survey. The majority (55.4 %) were 44 years or older (Baby Boomers and Generation X). Additionally, 37.8 % rated their knowledge of AI as none or limited, but 93.2 % expressed interest in learning more about AI. Many (79.7 %) perceived AI not to be fully used in radiation therapy but has increased its effectiveness in image registration, reconstruction, and contouring. With the increasing use of AI in healthcare, 96.0 % feel that AI may affect their role, and 82.4 % believe it may impact their job satisfaction. Educational supports indicated to be the most advantageous for their job were online modules (36.5 %) and in-person workshops (35.1 %).

Conclusion: Exploring the perspectives of radiation therapists has shown a strong interest in learning about AI and its role in radiation therapy. This information can help in understanding how to develop tailored strategies to mitigate potential barriers, leading to the successful implementation of AI in clinical radiation therapy practice.

Keywords: Artificial Intelligence; Educational Support; Perception; Radiotherapy.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Fig. 1
Fig. 1
Opinion of AI effectiveness on clinical applications.
Fig. 2
Fig. 2
Preferred learning methods and educational supports.
Fig. 3
Fig. 3
Aspects of AI that radiation therapists would like students to learn.
See this image and copyright information in PMC

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