Preferences for adopting artificial intelligence in radiation therapy treatment: A discrete choice experiment.

Objectives: The integration of artificial intelligence (AI) in radiation therapy offers significant potential to enhance cancer care by improving diagnostic accuracy, streamlining workflows, and reducing treatment delays. However, the adoption of AI in clinical settings depends heavily on its acceptability, shaped by perceptions of accuracy, cost, efficiency, and ethical considerations. This study explores the preferences of the Australian general population regarding the features of AI systems in radiation therapy.

Methods: A discrete choice experiment (DCE) was conducted with 533 respondents, who were representative of the Australian population. Participants were presented with hypothetical scenarios comparing AI systems described by attributes including accuracy, decision-making autonomy, impact on out-of-pocket costs, treatment timelines, and data privacy. Preferences were analysed using mixed logit and latent class models to evaluate heterogeneity and willingness-to-pay for AI system attributes.

Results: Respondents preferred AI systems with enhanced accuracy and reduced treatment delays. Systems less likely to misclassify tissues were highly valued, while fully autonomous AI systems were less favoured compared to assistive systems requiring clinician oversight. Data privacy concerns varied, with some participants prioritizing consent-based data usage. Heterogeneity analysis revealed four distinct preference classes, highlighting trade-offs between cost, speed, and ethical considerations. Willingness-to-pay estimates showed the respondents were willing to pay features such as enhanced oversight, reduced time burden, and AI-driven data use.

Conclusions: This study provides important information about Australian general public preferences for AI in treatment planning and can be used to inform future research on economic evaluations and implementation of AI-driven technologies in radiation therapy.