  The unstructured nature of clinical notes within electronic health records
often conceals vital patient-related information, making it challenging to
access or interpret. To uncover this hidden information, specialized Natural
Language Processing (NLP) models are required. However, training these models
necessitates large amounts of labeled data, a process that is both
time-consuming and costly when relying solely on human experts for annotation.
In this paper, we propose an approach that combines Large Language Models
(LLMs) with human expertise to create an efficient method for generating ground
truth labels for medical text annotation. By utilizing LLMs in conjunction with
human annotators, we significantly reduce the human annotation burden, enabling
the rapid creation of labeled datasets. We rigorously evaluate our method on a
medical information extraction task, demonstrating that our approach not only
substantially cuts down on human intervention but also maintains high accuracy.
The results highlight the potential of using LLMs to improve the utilization of
unstructured clinical data, allowing for the swift deployment of tailored NLP
solutions in healthcare.
