A Novel Framework to Integrate Data on Sex as a Biological Variable into Medical Education
This study provides one of the first empirical demonstrations of a scalable AI framework for streamlining curricular review and enhancing the integration of clinically relevant content on SABV in medical education. AI-assisted review offers a pathway to transform clinical care by ensuring that future physicians are provided with evidence-based data showing the influence of sex on health.
Authors
- Kelsey Martin (ORCID: https://orcid.org/0000-0002-0082-5040)
- Haleigh Larson
- Margaret A. Pisani (ORCID: https://orcid.org/0000-0001-8601-7038)
- Aeka Guru
- Carolyn M. Mazure
Institutions
- Yale University (US)
Publication Details
- Journal
- Journal of Women s Health
- Published
- 2025-12-03
- DOI
- https://doi.org/10.1177/15409996251405072
- Primary Topic
- Sex and Gender in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 0.00