FEMALE: THE COMPATIBILITY OF A PREDICTIVE MODEL FOR ENDOMETRIOSIS WITH THE PROTECTION OF WOMEN'S REPRODUCTIVE HEALTH DATA (TEST BioLaw Journal - Rivista di BioDiritto, 2024)
This contribution analyzes FEMaLe project, which develops a machine learning platform capable of analyzing omics data series and feeding information into a personalized predictive model to improve the women’s conditions affected by endometriosis. This study verifies the respect of the protection of female data with reference to Femtech app, which are not being clearly qualified as medical devices. Subsequently, will be analyzed the transfer of such data to third parties, without consent, starting from the American decision regarding the sharing with Facebook and Google, to underline the balance between health and privacy, analyzing the possible replacement with synthetic data.
Keywords: Endometriosis; Femtech; artificial intelligence; synthetic data; inverted privacy