Explainable Artificial Intelligence in valvular heart disease predominantly utilized Shapley Additive Explanations (66% of 52 studies), though rigorous clinical validation remains rare.
Systematic Review (n=52)
While XAI is actively researched for feature attribution in valvular heart disease, future work must focus on clinical validation and ensuring stability across patient populations.
Background Valvular heart disease (VHD) is a growing global health problem. Artificial intelligence (AI) models show promise for improving their diagnosis and management, but their black box nature limits transparency, making doctors hesitant to trust them. Explainable AI (XAI) aims to address this, but its application in VHD has not been systematically mapped. Methods We conducted a systematic review, searching for studies that applied XAI techniques to any type of VHD. From 374 records, 52 studies were included. Data were extracted on VHD types, AI/XAI methods, and the evaluation of explanations. Results Most research has focused on aortic stenosis and mitral regurgitation, using either structured patient data or imaging like echocardiograms. Shapley Additive Explanations was the dominant XAI method (66% of the studies), primarily for feature importance ranking. Although model performance was often strong, rigorous evaluation of explanations was rare. Only a few studies involved clinicians in assessing usefulness or used quantitative metrics to test reliability. Conclusion XAI is an active area of research in VHD, mainly for feature attribution. However, the field is still developing. To make XAI truly useful, future work must move beyond explanation generation to validating it with clinicians and ensuring they are stable and trustworthy across different patient populations.
Sathish et al. (Thu,) conducted a systematic review in Valvular heart disease (n=52). Explainable Artificial Intelligence (XAI) was evaluated on XAI methods and evaluation of explanations. Explainable Artificial Intelligence in valvular heart disease predominantly utilized Shapley Additive Explanations (66% of 52 studies), though rigorous clinical validation remains rare.