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Related Questions
- What are some common biases found in LLMs used in healthcare, and how can prompt engineering help mitigate them?
- How can the use of transparent and interpretable language in prompts improve the fairness of LLMs in healthcare decision-making?
- What are some strategies for designing inclusive and culturally sensitive prompts to improve the accuracy of LLMs in diverse patient populations?
- Can you discuss the importance of using real-world data and scenarios in prompts to improve the generalizability and reliability of LLMs in healthcare?
- How can prompt engineering techniques, such as data augmentation and adversarial training, be used to improve the robustness of LLMs to bias and errors?
- What role can human feedback and iteration play in the development and refinement of prompts for healthcare LLMs?
- How can the use of explainability techniques, such as saliency maps and feature importance, be integrated into prompt engineering to improve the transparency of LLMs in healthcare?
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