I am currently working on the development of artificial intelligence models for the analysis of medical images with the ultimate goal of transferring these tools to the clinical scenario as support systems for different actors in the medical field.

As part of this process, I focus on the validation and analysis of their algorithmic fairness prior to their effective integration with the other components of health information systems. To carry out these tasks I essentially use Python with libraries such as scikit-learn, Keras, PyTorch and django.

In my most recent work, we evaluated the state of the art of fairness in the field of medical image computing. We discuss the meaning of fairness in this area and comment on potential sources of bias, as well as available strategies to mitigate it. Finally, we analyze the current state of the field, identifying strengths and highlighting vacant areas, challenges and opportunities.

Recent Work

Addressing fairness in artificial intelligence for medical imaging

Nature Communications, 2022

Artificial intelligence in medical images. From theory to application (in Spanish)

delHospital Ediciones, 2021

Bias detection in Machine Learning models for dermoscopy analysis (in Spanish)

Fundación para el Desarrollo Regional, 2022

A technological Valquiria (in Spanish)

Innova Salud Digital, 2022

Valquiria an innovative project to fight skin cancer (in Spanish)

Innova Salud Digital, 2021

Data science in healthcare (in Spanish)

Innova Salud Digital, 2021

Experience

Education

Awards and Scholarships

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