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The use of diagnostic, prognostic, or more generally, decision support tools in healthcare, built from machine learning algorithms and artificial intelligence (AI), requires special attention. My research focuses on the crucial aspect of the robustness of machine learning (ML) models in healthcare. The objective of my thesis is to clearly define the various concepts of robustness that can be used to evaluate the robustness of an ML model in healthcare and identify those that should be prioritized to limit potential degradation of a model once deployed.

Research interests

  • Machine learning for Health
  • Trustworthy AI
  • Machine learning robustness



Conceptualizing and assessing the robustness of healthcare algorithms

Alan Balendran

Promotion : 2022

Supervisor.s : Raphaël Porcher

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