PainRelief.com: What are the main findings?
Response: The study found that neither GPT-4 nor Gemini exhibited significant racial, ethnic, or sex-based biases in their pain management recommendations. Both models frequently recommended opioids, but GPT-4 was more conservative in its recommendations, assigning higher pain severity ratings than Gemini. Gemini was more likely to recommend opioids, particularly strong ones like oxycodone, and suggested them later in treatment sequences. While the two LLMs had differences in their approaches, neither model’s opioid recommendations varied based on the race, ethnicity, or sex of the patient.
PainRelief.com: What should readers take away from your report?
Response: The findings suggest that LLMs could be promising tools in reducing bias in pain management. By offering consistent recommendations regardless of a patient’s racial or ethnic background, these models may help address the known disparities in opioid prescriptions, where minority patients have historically been undertreated for pain. LLMs could thus be valuable aids in ensuring more equitable treatment decisions in clinical settings.
PainRelief.com: What recommendations do you have for future research as a result of this study?
- Assess the performance of LLMs across a larger sample size and more diverse patient groups, including those of mixed racial backgrounds.
- Investigate how LLM recommendations align with current clinical guidelines to ensure their utility in real-world applications.
- Explore the role of patient preferences in opioid prescriptions and how LLMs handle individualized care decisions.
Disclosures: The study was supported in part by the National Institute of General Medical Sciences, but the findings and conclusions are those of the authors alone. There are no conflicts of interest to disclose.
PainRelief.com: Is there anything else you would like to add? Any disclosures?
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Last Updated on September 20, 2024 by PainRelief.com