Chatbots and Diabetes: Is There Gender Bias? Article Swipe
YOU?
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· 2025
· Open Access
·
· DOI: https://doi.org/10.1177/23743735251380954
This study evaluated 4 leading Large Language Models’ (LLMs) (ChatGPT-o1, DeepSeek-v3, Gemini 2.0 Flash, and Claude 3.7 Sonnet) responses to a question about Diabetic Retinopathy. Methods: The following questions were posed to the 4 LLMS: “I am a 52-year-old Caucasian [male/female] with out-of-control Type 2 Diabetes Mellitus, and I recently cannot read small print. What should I do?” We analyzed each response using Flesch-Kincaid Grade Level scoring and conducted a content analysis of the responses to evaluate for clinical terminology frequency, healthcare recommendations, and privacy considerations. Results: All platforms generated content at high school to college grade reading levels, exceeding recommended sixth-grade health literacy guidelines. DeepSeek incorporated more specialized clinical terminology and referenced specific diabetes guidelines not mentioned by ChatGPT, and exhibited greater gender discrepancy than the other 3 LLMs. Conclusion: While LLMs demonstrate promising capabilities for diabetes education, our results indicated that improvements in readability, gender bias mitigation, and risk of inappropriate output remain essential. Healthcare providers and physicians must review and monitor the answers before sharing with patients.
Related Topics
- Type
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Chatbots and Diabetes: Is There Gender Bias?Work title
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2025-09-01Full publication date if available
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Gloria Wu, Shikha Tewari, Adrial Wong, Eunah Chung, Ivan Chim, Ngoc Bich Hoang, Nikki Mansubi, Alireza Shams, Milan Del Buono, Shylaja Srinivasan, Hrishi Paliath-Pathiyal, Owais KhanList of authors in order
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| referenced_works | https://openalex.org/W2965117500, https://openalex.org/W2084678823, https://openalex.org/W2096648979, https://openalex.org/W2218255915, https://openalex.org/W4407605931, https://openalex.org/W4408279487, https://openalex.org/W4387616550, https://openalex.org/W2611652454, https://openalex.org/W4390227104, https://openalex.org/W4393029358, https://openalex.org/W2147057955, https://openalex.org/W4408781477, https://openalex.org/W4403583423, https://openalex.org/W4401831663, https://openalex.org/W2980742309, https://openalex.org/W4405326640, https://openalex.org/W4407680770, https://openalex.org/W4386776401, https://openalex.org/W4307370122, https://openalex.org/W4404233233, https://openalex.org/W4311102276 |
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