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Related Questions
- What role can knowledge graph-based input data play in enhancing LLM explainability in healthcare?
- How can the design of input prompts be used to elicit specific model behaviors and ensure transparent decision-making in clinical LLMs?
- Can you discuss the potential applications of attention weights and attribution methods in uncovering insights into LLM decision-making processes in healthcare?
- What measures can prompt engineers take to prevent biased or discriminatory results in healthcare LLMs and improve model accountability?
- In what ways can user feedback and testing be used to validate LLM explanations and improve the overall trustworthiness of these systems in healthcare settings?
- How might the incorporation of domain experts and subject matter specialists during prompt development facilitate more accurate and interpretable LLM results in healthcare?
- Can you explore the integration of multimodal input sources, such as images, videos, or audio, alongside natural language input to better inform and enhance LLM decision-making and explainability in healthcare applications?
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