  Large Language Models have difficulty communicating uncertainty, which is a
significant obstacle to applying LLMs to complex medical tasks. This study
evaluates methods to measure LLM confidence when suggesting a diagnosis for
challenging clinical vignettes. GPT4 was asked a series of challenging case
questions using Chain of Thought and Self Consistency prompting. Multiple
methods were investigated to assess model confidence and evaluated on their
ability to predict the models observed accuracy. The methods evaluated were
Intrinsic Confidence, SC Agreement Frequency and CoT Response Length. SC
Agreement Frequency correlated with observed accuracy, yielding a higher Area
under the Receiver Operating Characteristic Curve compared to Intrinsic
Confidence and CoT Length analysis. SC agreement is the most useful proxy for
model confidence, especially for medical diagnosis. Model Intrinsic Confidence
and CoT Response Length exhibit a weaker ability to differentiate between
correct and incorrect answers, preventing them from being reliable and
interpretable markers for model confidence. We conclude GPT4 has a limited
ability to assess its own diagnostic accuracy. SC Agreement Frequency is the
most useful method to measure GPT4 confidence.

Computation and Language;Artificial Intelligence