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 are increasingly applied to clinical decision-making. However%2C their potential to exhibit bias poses significant risks to clinical equity. Currently%2C there is a lack of benchmarks that systematically evaluate such clinical bias in LLMs. While in downstream tasks%2C some biases of LLMs can be avoided such as by instructing the model to answer I'm not sure...%2C the internal bias hidden within the model still lacks deep studies. We introduce CLIMB (shorthand for A Benchmark of Clinical Bias in Large Language Models)%2C a pioneering comprehensive benchmark to evaluate both intrinsic (within LLMs) and extrinsic (on downstream tasks) bias in LLMs for clinical decision tasks. Notably%2C for intrinsic bias%2C we introduce a novel metric%2C AssocMAD%2C to assess the disparities of LLMs across multiple demographic groups. Additionally%2C we leverage counterfactual intervention to evaluate extrinsic bias in a task of clinical diagnosis prediction. Our experiments across popular and medically adapted LLMs%2C particularly from the Mistral and LLaMA families%2C unveil prevalent behaviors with both intrinsic and extrinsic bias. This work underscores the critical need to mitigate clinical bias and sets a new standard for future evaluations of LLMs' clinical bias.?quality=80&w=800)
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