Explainable Boosting Machine for Choosing Radiation Dose-Volume Constraints on Cardio-Pulmonary Substructures Associated with Overall Survival in the NRG Oncology RTOG 0617 Clinical Trial

Lee, S.H.; Geng, H.; Arnold, Jacinta; Caruana, R.; Fan, Y.; Rosen, M.; Apte, A.; Deasy, J.O.; Xiao, Y.

doi:10.1016/j.ijrobp.2022.07.416

Cited by 2 publications

(2 citation statements)

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“…In terms of XAI methods, SHAP (twenty-five) and Grad-CAM (twenty) were the prevalent XAI choices for these studies, similarly to what found in the other papers employing XAI in combination with some kinds of evaluation (Figure 9). [172,181,[187][188][189] Grad-CAM ECG [190][191][192][193][194][195][196][197][198][199][200]] EHR [199] CMR [199] Others [187,192,[201][202][203][204][205] Grad-CAM++ ECG […”

Section: No Evaluation Methodsmentioning

confidence: 99%

A Review of Evaluation Approaches for Explainable AI With Applications in Cardiology

Salih,

Boscolo Galazzo,

Gkontra

et al. 2023

Preprint

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<p>Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.</p>

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