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Machine Learning in Medicine: To Explain, or Not to Explain, That Is the Question
Cedric Bousquet, Diva Beltramin
114 - 115
10.3233/SHTI220407
Research Article
Studies in Health Technology and Informatics
Volume 294: Challenges of Trustable AI and Added-Value on Health
Abstract

In 2022, the Medical Informatics Europe conference created a special topic called “Challenges of trustable AI and added-value on health” which was centered around the theme of eXplainable Artificial Intelligence. Unfortunately, two opposite views remain for biomedical applications of machine learning: accepting to use reliable but opaque models, vs. enforce models to be explainable. In this contribution we discuss these two opposite approaches and illustrate with examples the differences between them.

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