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This article aims to provide a pragmatic basis for designing and executing rigorous evaluations of healthcare LLMs by describing underlying concepts and potential pitfalls, while emphasizing the importance of aligning evaluation methods with the research question.
On complex ID scenarios, large language models responses were variable and caution is required when deploying these models in ID domains without specialist oversight, suggesting caution is required when deploying these models in ID domains without specialist oversight.
This Perspective concerns the most consequential of these applications: the autonomous triage of self-presenting, undifferentiated patients, with little or no clinician in the loop, and the evidence of safety does not yet exist.
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