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Related Questions
- How do medical large language models handle implicit bias and stereotyping in their representations of patients and healthcare professionals?
- Can you explain the concept of representation in medical LLMs and how it affects the accuracy and fairness of diagnosis and treatment recommendations?
- In what ways can diverse perspectives and experiences be incorporated into the training data of medical LLMs to improve their representation of patient needs and preferences?
- How do medical LLMs use representation learning to capture complex relationships between patient symptoms, medical history, and treatment outcomes?
- What are the benefits and challenges of using representation learning in medical LLMs, particularly in relation to patient data from underrepresented populations?
- Can you provide examples of how medical LLMs can be designed to represent diverse patient populations, including those with rare or chronic conditions?
- How do medical LLMs balance the need for accurate representation with the risk of perpetuating existing health disparities and biases in their training data?
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