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Related Questions
- What are the potential risks of relying on opaque LLMs in medical decision-making, and how can they be addressed through explainability techniques?
- How can healthcare organizations ensure that LLMs are transparent and accountable in their decision-making processes, particularly in high-stakes situations?
- What are the potential consequences of using LLMs in healthcare without proper validation and testing, and how can these risks be mitigated through rigorous evaluation and deployment strategies?
- In what ways can LLMs be designed to provide transparent and interpretable results, and what are the benefits of doing so in healthcare applications?
- How can healthcare professionals be trained to effectively use and interpret the outputs of LLMs, particularly in complex and high-stakes situations?
- What are the potential consequences of relying on LLMs for diagnosis or treatment recommendations, and how can these risks be mitigated through human oversight and review?
- How can healthcare organizations balance the benefits of using LLMs with the need for transparency and accountability in medical decision-making, particularly in cases where LLMs may be biased or inaccurate?
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