@article {43321,
	title = {Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis},
	journal = {Diagnostics},
	volume = {13},
	number = {2345},
	year = {2023},
	month = {07/2023},
	publisher = {MDPI},
	abstract = {Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of {\textquotedblleft}black box{\textquotedblright} models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes.},
	keywords = {electrocardiograms, Explainable artificial intelligence, heat maps},
	doi = {https://doi.org/10.3390/diagnostics13142345},
	author = {Stor{\r a}s, Andrea and Ole Emil {Andersen} and Lockhart, Sam and Thielemann, Roman and Gnesin, Filip and Thambawita, Vajira and Hicks, Steven and Kanters, J{\o}rgen and Str{\"u}mke, Inga and Halvorsen, P{\r a}l and Riegler, Michael}
}
