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Related Questions
- What are the common types of data drift and concept drift that can occur in healthcare language models?
- How can active learning be used to detect and adapt to changes in clinical terminology and nomenclature in healthcare LLMs?
- What are the benefits and limitations of using active learning for addressing concept drift in healthcare LLMs, particularly in high-stakes applications?
- How can active learning be integrated with other techniques, such as transfer learning and meta-learning, to improve the robustness of healthcare LLMs to concept drift?
- What are some strategies for selecting the most informative and representative samples for active learning in healthcare LLMs, given the high dimensionality of medical data?
- Can active learning be used to adapt healthcare LLMs to changes in population demographics, such as age, sex, or ethnicity, and if so, how?
- How can active learning be used to address concept drift in healthcare LLMs when the underlying distribution of the data is not well-characterized or is subject to sudden shifts?
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