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Related Questions
- What are some key considerations for designing effective prompts for healthcare-related LLMs to ensure accurate and reliable outputs?
- How can prompt engineering techniques, such as data augmentation and adversarial training, be applied to improve the robustness and generalizability of LLMs in healthcare?
- What role can multi-modal input and output formats play in enabling LLMs to handle diverse clinical scenarios and patient populations?
- Can you discuss the importance of incorporating domain-specific knowledge and ontologies into LLM prompts to improve their understanding of medical concepts and terminology?
- How can prompt engineering be used to address issues of bias and fairness in LLMs, particularly in the context of healthcare decision-making?
- What are some strategies for evaluating and validating the performance of LLMs in healthcare applications, and how can prompt engineering contribute to this process?
- Can you describe the potential benefits and challenges of using transfer learning and fine-tuning techniques to adapt LLMs for specific healthcare domains and populations?
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