Evidence map›Paper›PMID 39896145›Full record

ArticleTechnical innovations & patient support in radiation oncology2025

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

Caroline Marr, Yat Tsang

Abstract read
In one paragraph

Article in Technical innovations & patient support in radiation oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Caroline MarrRadiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.
Yat TsangRadiation Medicine Program, Princess Margaret Cancer Centre, Toronto, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Artificial IntelligenceEducational SupportPerceptionRadiotherapy

Identifiers

PMID39896145
PMCPMC11782823

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.