Ontology for Healthcare AI Privacy in Brazil

This article details the creation of a novel domain ontology at the intersection of epidemiology, medicine, statistics, and computer science. It outlines a systematic approach to handling structured data anonymously in preparation for its use in Artificial Intelligence (AI) applications in healthcare. The development followed 7 steps, including defining scope, selecting knowledge, reviewing important terms, constructing classes that describe designs used in epidemiological studies, machine learning paradigms, types of data and attributes, risks that anonymized data may be exposed to, privacy attacks, techniques to mitigate re-identification, privacy models, and metrics for measuring the effects of anonymization. It concludes with a practical implementation of this ontology in hospital settings to develop and validate AI systems.