Gender Representation of Health Care Professionals in Large Language Model–Generated Stories
This cross-sectional study highlights the need for LLM developers to update their tools for equitable and diverse gender representation in essential health care roles, including medical doctors, surgeons, and nurses. As LLMs become increasingly adopted throughout health care and education, continuous monitoring of these tools is needed to ensure that they reflect a diverse workforce, capable of serving society's needs effectively.
Authors
- Andrew Rowland (ORCID: https://orcid.org/0000-0002-8946-3954)
- Nicole M. Kuderer (ORCID: https://orcid.org/0000-0002-9263-8540)
- Benjamin Chin‐Yee (ORCID: https://orcid.org/0000-0003-0737-3603)
- Ashley M. Hopkins (ORCID: https://orcid.org/0000-0001-7652-4378)
- Michael J. Sorich (ORCID: https://orcid.org/0000-0003-1999-866X)
- Jessica M. Logan (ORCID: https://orcid.org/0000-0003-4046-6908)
- Bradley D. Menz (ORCID: https://orcid.org/0000-0002-0855-5081)
Institutions
- Western University (CA)
- Flinders University (AU)
- University of South Australia (AU)
- University of Cambridge (GB)
Publication Details
- Journal
- JAMA Network Open
- Published
- 2024-09-23
- DOI
- https://doi.org/10.1001/jamanetworkopen.2024.34997
- Citations
- 13
- Primary Topic
- Diversity and Career in Medicine
- Type
- article
- Field-Weighted Citation Impact
- 12.09
Funders
- Pfizer
- Johns Hopkins University
- Medical Research Council
- National Health and Medical Research Council
- Cancer Council South Australia