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Related Questions
- What are some potential biases in healthcare-related language that can affect the performance of large language models (LLMs) and how can prompt engineering address these biases?
- How can prompt engineering be used to reduce disparities in healthcare outcomes by creating more inclusive and culturally sensitive language models?
- What are some strategies for using prompt engineering to identify and mitigate the impact of bias in medical terminology and jargon on LLMs?
- Can prompt engineering help to reduce disparities in healthcare outcomes by promoting more nuanced and empathetic responses from LLMs to sensitive patient concerns?
- How can prompt engineering be used to create more accurate and relevant responses from LLMs in low-resource languages, where healthcare disparities are often more pronounced?
- What role can prompt engineering play in reducing health disparities by improving the accessibility and usability of LLM-based healthcare services for marginalized populations?
- How can prompt engineering be used to develop more personalized and patient-centered language models that take into account individual patient needs and preferences, reducing disparities in healthcare outcomes?
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