Due to the fact that Gaucher disease is a rare disease with few national registries, the computational power of a local computer for the study of correlations with other diseases was enough to analyse the data collected. The challenge now is to generate a new model able to predict if a person has the probability of developing Gaucher disease. In this case, the AI model must include not only data from current Gaucher disease patients but also data from healthy patients. Opening our sample universe also to healthy patients exponentially increases the sample size (from hundreds to millions) and potentially the model’s complexity. This implies the need of advanced computational resources such as the cloud platform provided by EOSC. Although this proof of concept is focused in Gaucher disease, the developed solution could be adapted in the future to other diseases data bases. The obtained general-purpose solution will be exploited by Kampal Data Solutions in the mid-term. | 