Improving reliability and accuracy of structured data extraction using a consensus large-language model approach–a use case description in multiple sclerosis

Our method enables fast and reliable LLM-based analysis of large clinical routine data sets of varying complexity with a low technical barrier to entry. By generating an LLM consensus, we were able to considerably improve the quality of the output making it comparable to data created by neurology specialists. This approach allows large amounts of unstructured data to be analyzed in a time and cost-efficient manner. Nevertheless, the evaluation of errors in results produced by LLM remains difficult. Scientific work using such methods must continue to be subject to strict testing of the validity of the method in the future.