<ul><li>This study aims to enhance interpretability and explainability of multi-modal prediction models integrating imaging and tabular patient data.</li><li>The adapted xAI methods were used to generate explanation maps for identification of relevant image features and error analysis.</li><li>The dTMs achieve state-of-the-art prediction performance, with area under the curve (AUC) values close to 0.8.</li><li>Explanation maps calculated from brain imaging dTMs for functional outcome highlighted critical brain regions such as the frontal lobe.</li></ul>

Going beyond explainability in multi-modal stroke outcome prediction models

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