The value of doubt: training LLMs to consider diagnostic uncertainty may improve clinical utility

Abstract

While physicians routinely consider uncertainty during patient diagnosis, large language models (LLMs) often fail to recognize that real-world clinical data can be too limited for a definitive diagnosis. Zhou et al. address this problem by training a LLM, ConfiDx, to recognize medical cases with limited clinical data. This approach improves the utility of LLMs in the clinic and enables physicians to more effectively recognize and explain uncertainty in their patient care.

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