Leveraging large language models to mimic domain expert labeling in unstructured text-based electronic healthcare records in non-english languages

Fine-tuned LLMs can categorize unstructured EHR data with high accuracy, closely approximating the performance of domain experts. This approach significantly reduces the time and costs associated with manual data labeling, demonstrating the potential to streamline the processing of large-scale healthcare data for AI applications.