**1. Introduction**

The majority of Machine Learning (ML) systems require the participation of humans at several steps of the AI pipeline. With this requirement in mind, new types of interactions between humans and machine learning algorithms are being defined, which we can group under the term Human-in-the-Loop Machine Learning (HITL-ML) [1]. The goal is to make machine learning models more accurate, obtain the desired accuracy faster, and also make humans more efficient when training or using a ML model.

In the health domain (and others), due to the reduced number of datasets, traditional ML approaches suffer from insufficient training samples [2]. Using specific techniques as the ones described in this proposal could help improving both the training process and the final user performance.
