Explainable AI in healthcare: to explain, to predict, or to describe?

Alex Carriero¹, Anne de Hond², Bram Cappers³, Fernando Paulovich³, Sanne Abeln⁴, Karel Gm Moons², Maarten van Smeden²

¹Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. a.j.carriero@umcutrecht.nl.

²Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.

³Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands.

⁴Department of Information and Computing Sciences, Utrecht University, Utrecht, The Netherlands.

PubMed PMC DOI PDF

Abstract

Explainable Artificial Intelligence (AI) methods are designed to provide information about how AI-based models make predictions. In healthcare, there is a widespread expectation that these methods will provide relevant and accurate information about a model's inner-workings to different stakeholders (ranging from patients and healthcare providers to AI and medical guideline developers). This is a…

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