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Related Questions
- What are the key challenges in using prompt engineering to improve the interpretability of LLMs in healthcare, such as limitations in understanding user intent and clinical context?
- How do the complexities of medical language and nuanced clinical decision-making impact the effectiveness of prompt engineering in healthcare?
- What are some potential biases in LLMs that can be introduced through prompt engineering in the healthcare domain, such as gender or racial biases?
- How can the lack of standardized medical terminology and protocols affect the interpretability of LLMs in healthcare when using prompt engineering?
- What are the potential risks of over-reliance on prompt engineering in healthcare, such as decreased clinician trust and increased liability?
- Can the use of natural language processing (NLP) and machine learning (ML) in prompt engineering be used to identify and mitigate these challenges in healthcare?
- What are some potential strategies for addressing the limitations of prompt engineering in healthcare, such as using multimodal input or incorporating domain expertise?
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