Abstract
Background: Large language models (LLMs) such as OpenAI's GPT-4 or Google's PaLM 2 are proposed as viable diagnostic support tools or even spoken of as replacements for "curbside consults". However, even LLMs specifically trained on medical topics may lack sufficient diagnostic accuracy for real-life applications. Methods: Using collective intelligence methods and a dataset of 200 clinical vigne