Mixed methods assessment of the influence of demographics on medical advice of ChatGPT
Open accessAbstract
Objectives: To evaluate demographic biases in diagnostic accuracy and health advice between generative artificial intelligence (AI) (ChatGPT GPT-4) and traditional symptom checkers like WebMD. Materials and Methods: Combination symptom and demographic vignettes were developed for 27 most common symptom complaints. Standardized prompts, written from a patient perspective, with varying demographic permutations of age, sex, and race/ethnicity were entered into ChatGPT (GPT-4) between July and August 2023. In total, 3 runs of 540 ChatGPT prompts were compared to the corresponding WebMD Symptom Checker output using a mixed-methods approach. In addition to diagnostic correctness, the associated text generated by ChatGPT was analyzed for readability (using Flesch-Kincaid Grade Level) and qualitative aspects like disclaimers and demographic tailoring. Results: ChatGPT matched WebMD in 91% of diagnoses, with a 24% top diagnosis match rate. Diagnostic accuracy was not significantly different across demographic groups, including age, race/ethnicity, and sex. ChatGPT's urgent care recommendations and demographic tailoring were presented significantly more to 75-year-olds versus 25-year-olds (P < .01) but were not statistically different among race/ethnicity and sex groups. The GPT text was suitable for college students, with no significant demographic variability. Discussion: The use of non-health-tailored generative AI, like ChatGPT, for simple symptom-checking functions provides comparable diagnostic accuracy to commercially available symptom checkers and does not demonstrate significant demographic bias in this setting. The text accompanying differential diagnoses, however, suggests demographic tailoring that could potentially introduce bias. Conclusion: These results highlight the need for continued rigorous evaluation of AI-driven medical platforms, focusing on demographic biases to ensure equitable care.
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Factors such as institutional culture, faculty demographics, and prior exposure to technology can influence the effectiveness of GenAI integration strategies.
“Factors such as institutional culture, faculty demographics, and prior exposure to technology can influence the effectiveness of GenAI integration strategies [8, 17]”
From Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education (2025) · cites this paper as [8]
The Introduction (page unavailable) discusses how patient demographic information in prompts may affect ChatGPT’s medical advice, not how faculty demographics affect GenAI integration. Searches for institutional culture, faculty demographics, prior technology exposure, and integration strategies found no passage addressing the claimed factors. (LLM, conf=0.78)
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A scoping review of bias in large AI models for medicine and healthcare
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- Authors
- Andreadis, K., Newman, D. R., Twan, C., Shunk, A., Mann, D. M., & Stevens, E. R.
- Affiliations
- New York University (US)
- Brown University (US)
- NYU Langone Health (US)
- Corresponding author
- Stevens, Elizabeth R
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-04-29
- Volume
- 31
- Issue
- 9
- Pages
- 2002-2009
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC11339520
- OpenAlex
- W4396228125
- Cited by
- 20
- References
- 38
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 7
- FWCI
- 1.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- ChatGPT, generative AI, demographic bias, health advice, symptom checkers, diagnostic accuracy, readability, age differences, sex differences, race and ethnicity, health care equity
- MeSH terms
- Sociodemographic Factors, Adult, Artificial Intelligence, Demography, Female, Humans, Male
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