Realistic Counterfactual Explanations for Clinical AI-decision Aid on Computed Tomography for Adaptive Radiotherapy

Abstract

Adaptive radiotherapy (ART) aims to detect changing patient anatomy and adapt the radiotherapy (RT) treatment plan accordingly, improving accuracy and outcomes. Artificial intelligence (AI) is introduced into this workflow to facilitate adaptation and manage the increased workload. However, the complexity and opacity of AI models used in ART pose challenges for clinical adoption. Outputs of traditional explainable AI methods, such as heatmaps, often lack consistency and can induce cognitive bias in clinicians' decision-making. This study introduces a novel approach using counterfactual explanations and domain knowledge to enhance transparency and trust in ART systems. By leveraging the physical properties of computed tomography images the proposed method generates realistic and semantically meaningful counterfactuals. These explanations help clinicians understand the clinical relevance of AI-detected deviations in the treatment, supporting safer and more effective decision-making for RT.

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