Precision medicine aims to tailor prevention and treatment to each person, based on the genetic and environmental factors that shape their health. AI can help by finding relevant patterns in large amounts of biomedical data. This review examines how sex and gender, which contribute to differences in health and disease, are taken into account, or not, in the AI technologies used in this field.
Differences between women and men have been reported in how many diseases develop, how common they are, how their symptoms present and how they respond to treatment, including in cardiovascular disease, diabetes and mental health. Yet, according to the authors, most biomedical AI technologies do not include ways of detecting bias, and most algorithms ignore sex and gender altogether. This can lead to poorer results, errors and discriminatory outcomes. In depression, for example, women are diagnosed more often than men, but this may partly reflect the fact that research has historically measured symptoms that are more common in women.
The authors review the existing sex and gender gaps in several technologies used in precision medicine, and make recommendations on how to use them in ways that improve health outcomes for everyone and reduce inequalities.
Learn more about this review here: https://doi.org/10.1038/s41746-020-0288-5
Reference:
Cirillo, D., Catuara-Solarz, S., Morey, C., Guney, E., Subirats, L., Mellino, S., Gigante, A., Valencia, A., Rementeria, M.J., Chadha, A.S., & Mavridis, N. (2020). Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. npj Digital Medicine, 3, 81.