Evidence map›Paper›PMID 39705152›Full record

ArticleJournal of medical radiation sciences2025

Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment.

Milena Lewandowska, Deborah Street, Jackie Yim, Scott Jones, Rosalie Viney

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of medical radiation sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
–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 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  3. Review
  4. Article
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

5 authors.

Milena LewandowskaCentre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-9068-9504
Deborah StreetCentre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-4476-0656
Jackie YimCentre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-3527-260X
Scott JonesRadiation Oncology Princess Alexandra Hospital Raymond Terrace, Brisbane, Queens Land, Australia.ORCID https://orcid.org/0000-0001-8310-6393
Rosalie VineyCentre for Health Economics Research and Evaluation, University of Technology Sydney, Sydney, New South Wales, Australia.ORCID https://orcid.org/0000-0002-0039-9635

Funding

Digital Health Collaborative Research Centre
6 · The paper itself

Abstract

introductionThe application of artificial intelligence (AI) in radiation therapy holds promise for addressing challenges, such as healthcare staff shortages, increased efficiency and treatment planning variations. Increased AI adoption has the potential to standardise treatment protocols, enhance quality, improve patient outcomes, and reduce costs. However, drawbacks include impacts on employment and algorithmic biases, making it crucial to navigate trade-offs. A discrete choice experiment (DCE) was undertaken to examine the AI-related characteristics radiation oncology professionals think are most important for adoption in radiation therapy treatment planning.

methodsRadiation oncology professionals completed an online discrete choice experiment to express their preferences about AI systems for radiation therapy planning which were described by five attributes, each with 2-4 levels: accuracy, automation, exploratory ability, compatibility with other systems and impact on workload. The survey also included questions about attitudes to AI. Choices were modelled using mixed logit regression.

resultsThe survey was completed by 82 respondents. The results showed they preferred AI systems that offer the largest time saving, and that provide explanations of the AI reasoning (both in-depth and basic). They also favoured systems that provide improved contouring precision compared with manual systems. Respondents emphasised the importance of AI systems being cost-effective, while also recognising AI's impact on professional roles, responsibilities, and service delivery.

conclusionsThis study provides important information about radiation oncology professionals' priorities for AI in treatment planning. The findings from this study can be used to inform future research on economic evaluations and management perspectives of AI-driven technologies in radiation therapy.

Indexed as

Artificial IntelligenceChoice BehaviorRadiotherapy Planning, Computer-AssistedFemaleHumansMaleRadiation OncologySurveys and Questionnairesartificial intelligenceDCEdiscrete choice experimentpreferencesradiation oncology professionalsradiation therapy planningwillingness‐to‐save time

Identifiers

PMID39705152
PMCPMC12449612

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.