A new study finds that large language models (LLMs), used with straightforward prompting, perform poorly on routine number-crunching tasks that hospital administrators depend on every day to track patients and allocate resources. The findings have been published in PLOS Digital Health by Eyal Klang of the Icahn School of Medicine at Mount Sinai, New York, U.S., and colleagues.
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