Maximizing impact of explainable artificial intelligence in radiotherapy: a critical review
Abstract
Objective. Artificial intelligence (AI) can enable automation, improve treatment accuracy, allow for a more efficient workflow, and improve the cost-effectiveness of radiotherapy (RT). To implement AI in RT, clinicians have expressed a desire to understand the AI outputs. Explainable AI (XAI) methods have been put forward as a solution, but the multidisciplinary nature of RT complicates the application of trustworthy and understandable XAI methods. The objective of this review is to analyze XAI in the RT landscape and understand how XAI can best support the diverse user groups in RT by exploring challenges and opportunities with a critical lens. Approach . We performed a review of XAI in RT, evaluating how explanations are built, validated, and embedded across the RT workflow, with attention to XAI purposes, evaluation and validation, interpretability trade-offs, and RT’s multidisciplinary context. Main results . XAI in RT serves five purposes: (1) knowledge discovery, (2) model verification, (3) model improvement, (4) clinical verification, and (5) clinical justification/actionability. Many studies favor interpretability but neglect fidelity and seldom include user-specific evaluation. Key challenges include stakeholder diversity, evaluation of XAI, cognitive bias, and causality; we also outline opportunities. Significance . By linking XAI purposes to RT tasks and highlighting challenges and opportunities, we provide actionable recommendations and a user-centric framework to guide the development, validation, and deployment of XAI in RT.
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Many existing XAI tools in Radiation Oncology rely on single-modality data or exclude unstructured data sources such as clinical narratives and imaging.
“Many existing XAI tools in Radiation Oncology rely on single-modality data24 or exclude unstructured data sources such as clinical narratives and imaging.25,26”
From X-CART: a multimodal real-world data pipeline for explainable AI-driven patient navigation in radiation oncology (2026) · cites this paper as [24]
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“XAI encompasses various approaches to make AI models more transparent and understandable (Gunning et al 2019, Vilone and Longo 2021, Tjoa and Guan 2021, Linardatos et al 2021, Ali et al 2023).”
The false hope of current approaches to explainable artificial intelligence in health care
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“…should be avoided altogether and use transparent models instead, Herzog (2022) contends that explainability comes at an undesirable cost of performance, and similarly, Ghassemi et al (2021) describe how the interpretability of XAI in healthcare is currently unsuitable for healthcare cases.”
Notions of explainability and evaluation approaches for explainable artificial intelligence
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Bias in AI-based models for medical applications: challenges and mitigation strategies
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Artificial Intelligence and Patient-Centered Decision-Making
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Zink, J. A., Rashid, R., Chinthala, L., Kheirinejad, S., White, B. M., Kheirkhah Rahimabad, P., Hashtarkhani, S., Kumsa, F. A., Brett, C. L., Davis, R. L., et al. (2026) · ESMO Real World Data and Digital Oncology · doi:10.1016/j.esmorw.2026.100771 · Cited by 1 · see citing works
- Authors
- Heising, L. M., Wolfs, C. J. A., Ou, C. X. J., Hoebers, F. J. P., van Limbergen, E. J., Verhaegen, F., & Jacobs, M. J. G.
- Venue
- Physics in Medicine & Biology
- Publisher
- IOP Publishing
- Published
- 2026-02-06
- Volume
- 71
- Issue
- 3
- Pages
- 03TR01
- ISSN
- 0031-9155
- 1361-6560
- Cited by
- 2
- References
- 13
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