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- W4297477913 abstract "Building trust and transparency in healthcare can be achieved using eXplainable Artificial Intelligence (XAI), as it facilitates the decision-making process for healthcare professionals. Knowledge graphs can be used in XAI for explainability by structuring information, extracting features and relations, and performing reasoning. This paper highlights the role of knowledge graphs in XAI models in healthcare, considering a state-of-the-art review. Based on our review, knowledge graphs have been used for explainability to detect healthcare misinformation, adverse drug reactions, drug-drug interactions and to reduce the knowledge gap between healthcare experts and AI-based models. We also discuss how to leverage knowledge graphs in pre-model, in-model, and post-model XAI models in healthcare to make them more explainable." @default.
- W4297477913 created "2022-09-29" @default.
- W4297477913 creator A5021000866 @default.
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- W4297477913 date "2022-09-28" @default.
- W4297477913 modified "2023-09-30" @default.
- W4297477913 title "Knowledge Graphs and Explainable AI in Healthcare" @default.
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- W4297477913 doi "https://doi.org/10.3390/info13100459" @default.
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