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In Medical physics ; h5-index 59.0

The development of medical imaging AI systems for evaluating COVID-19 patients has demonstrated potential for improving clinical decision-making and assessing patient outcomes during the recent COVID-19 pandemic. These have been applied to many medical imaging tasks including disease diagnosis and patient prognosis, as well as augmented other clinical measurements to better inform treatment decisions. Because these systems are used in life-or-death decisions, clinical implementation relies on user trust in the AI output. This has caused many developers to utilize explainability techniques in an attempt to help a user understand when an AI algorithm is likely to succeed as well as which cases may be problematic for automatic assessment, thus increasing the potential for rapid clinical translation. AI application to Covid-19 has been marred with controversy recently. This review discusses several aspects of explainable and interpretable AI as it pertains to the evaluation of COVID-19 disease and it can restore trust in AI application to this disease. This includes the identification of common tasks that are relevant to explainable medical imaging AI, an overview of several modern approaches for producing explainable output as appropriate for a given imaging scenario, a discussion of how to evaluate explainable AI, and recommendations for best practices in explainable/interpretable AI implementation. This review will allow developers of AI systems for COVID-19 to quickly understand the basics of several explainable AI techniques and assist in selection of an approach that is both appropriate and effective for a given scenario. This article is protected by copyright. All rights reserved.

Fuhrman Jordan D, Gorre Naveena, Hu Qiyuan, Li Hui, El Naqa Issam, Giger Maryellen L


AI, COVID-19, deep learning, explainability, interpretability