  This study evaluates whether large language models (LLMs) exhibit biases
towards medical professionals. Fictitious candidate resumes were created to
control for identity factors while maintaining consistent qualifications. Three
LLMs (GPT-4, Claude-3-haiku, and Mistral-Large) were tested using a
standardized prompt to evaluate resumes for specific residency programs.
Explicit bias was tested by changing gender and race information, while
implicit bias was tested by changing names while hiding race and gender.
Physician data from the Association of American Medical Colleges was used to
compare with real-world demographics. 900,000 resumes were evaluated. All LLMs
exhibited significant gender and racial biases across medical specialties.
Gender preferences varied, favoring male candidates in surgery and orthopedics,
while preferring females in dermatology, family medicine, obstetrics and
gynecology, pediatrics, and psychiatry. Claude-3 and Mistral-Large generally
favored Asian candidates, while GPT-4 preferred Black and Hispanic candidates
in several specialties. Tests revealed strong preferences towards Hispanic
females and Asian males in various specialties. Compared to real-world data,
LLMs consistently chose higher proportions of female and underrepresented
racial candidates than their actual representation in the medical workforce.
GPT-4, Claude-3, and Mistral-Large showed significant gender and racial biases
when evaluating medical professionals for residency selection. These findings
highlight the potential for LLMs to perpetuate biases and compromise healthcare
workforce diversity if used without proper bias mitigation strategies.
