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The paper discusses the use of Generative AI models, such as GANs, VAEs, and Diffusion Models, for predicting medical device failures, emphasizing the importance of explainability and reliability in safety-critical applications. It proposes a systematic framework that combines feature attribution methods to enhance interpretability and assess model reliability through robustness and uncertainty quantification. The findings suggest that explainable generative models can improve predictive performance and pro

Author

Editor IJTSRD

Language

EN