Fairness in AI-Driven Oncology: Investigating Racial and Gender Biases in Large Language Models
To our knowledge, this is the first study of its kind to investigate racial and gender biases of such a diverse set of AI chatbots, and that too, within oncology. The methodology presented in this study provides a framework for targeted bias evaluation of LLMs in various fields across medicine.
Authors
- Anjali Agrawal (ORCID: https://orcid.org/0000-0002-6725-7500)
Institutions
- The University of Texas at Austin (US)
Publication Details
- Journal
- Cureus
- Published
- 2024-09-16
- DOI
- https://doi.org/10.7759/cureus.69541
- Citations
- 8
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.96