Preferences for Adopting Artificial Intelligence in Radiation Therapy Treatment: A Discrete Choice Experiment
Author(s)
Milena Lewandowska, MPhil1, Deborah Street, PhD2, Jackie Yim, PhD2, Scott Jones, PhD3, Rosalie Viney, PhD2.
1Research Fellow, University of Technology Sydney, Sydney, Australia, 2University of Technology Sydney, Sydney, Australia, 3Princess Alexandra Hospital, Brisbane, Australia.
1Research Fellow, University of Technology Sydney, Sydney, Australia, 2University of Technology Sydney, Sydney, Australia, 3Princess Alexandra Hospital, Brisbane, Australia.
Presentation Documents
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 underscored the importance of balancing accuracy with affordability.
CONCLUSIONS: This study provides important information about Australian general public preferences 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.
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 underscored the importance of balancing accuracy with affordability.
CONCLUSIONS: This study provides important information about Australian general public preferences 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.
Conference/Value in Health Info
2025-09, ISPOR Real-World Evidence Summit 2025, Tokyo, Japan
Value in Health Regional, Volume 49S (September 2025)
Code
RWD10
Topic Subcategory
Data Protection, Integrity, & Quality Assurance
Disease
SDC: Oncology