Sex in the Medical Machine: The GenderSci Lab Analyzes the Algorithmic Future of Sex-Based Medicine
Artificial intelligence and machine learning tools offer hope for developing earlier, more tailored risk assessment and disease treatment. Across a wide range of biomedical fields, researchers have begun to incorporate gender and sex variables into precision medicine algorithms, including in well-funded research initiatives. The GenderSci Lab, with its focus on concepts and methods in sex and gender science, has begun to explore the implications of this algorithmic future for women’s and men’s health and the health of sex and gender minorities.
In a peer-reviewed paper recently published in Big Data and Society and led by Kelsey Ichikawa and Marion Boulicault, GenderSci Lab members explore the use of sex stratification in applications of machine learning in medical research and argue that current practices risk embedding biological sex essentialist assumptions into medical science. These practices include the creation of distinct algorithms for males and females (what we call “pink and blue algorithms”), the use of machine learning to identify distinct male and female patterns in disease, and the incorporation of gender/sex variables as predictors in algorithms for disease risk and detection.
To analyze sex-stratified algorithmic approaches, we looked at the field of Alzheimer’s research, where pronounced gender/sex differences in disease distribution have fueled interest in how sex- and gender-sensitive approaches might improve prevention, screening, diagnosis, and treatment. Featuring case studies of research programs integrating sex stratified algorithms in studies of Alzheimer’s Disease, the Big Data and Society paper identifies three ways that sex-stratified precision medicine algorithms may distort our understanding of sex and gender factors in health outcomes: (1) effacing contested knowledge, (2) obscuring social factors, (3) ossifying the sex binary.
To build awareness and critical dialogue about these potential issues, the GenderSci Lab, in partnership with the Robert Wood Johnson Foundation, developed a video explainer for use in teaching and sharing across diverse communities. As explained in the video, we found that uncritical incorporation of sex categories in algorithmic approaches to Alzheimer’s risks “perpetuating crude ontologies of sex and gender that undermine both scientific validity and health justice” (Ichikawa & Boulicault, p. 1).
With our interdisciplinary research team of philosophers, historians, and sociologists of science and medicine and biomedical scientists, public health experts, and data scientists, the GenderSci Lab hopes to bring insight, clarity, and ethical reflection as algorithmic approaches to sex and gender medicine accelerate in medical research and in the clinic.
Read the open access article here: Ichikawa, Kelsey, Marion Boulicault, Alex Thinius, Marina DiMarco, Audrey R. Murchland, Ben Maldonado, Abigail S. Higgins, and Sarah S. Richardson. “Sex in the Medical Machine: How Algorithms Can Entrench Bioessentialism in Precision Medicine”. Big Data and Society 12, no. 4 (2025).
SUGGESTED CITATION
Ichikawa, K. and Richardson, S. “Sex in the Medical Machine: The GenderSci Lab Analyzes the Algorithmic Future of Sex-Based Medicine.” Gendersci Lab Blog. 27 January 2026. https://www.genderscilab.org/blog/sex-in-the-medical-machine.
STATEMENT OF INTELLECTUAL LABOR
This blog post was drafted by Kelsey Ichikawa and Sarah S. Richardson with input and edits from Marion Boulicault.