  Although supervised machine learning is popular for information extraction
from clinical notes, creating large annotated datasets requires extensive
domain expertise and is time-consuming. Meanwhile, large language models (LLMs)
have demonstrated promising transfer learning capability. In this study, we
explored whether recent LLMs can reduce the need for large-scale data
annotations. We curated a manually-labeled dataset of 769 breast cancer
pathology reports, labeled with 13 categories, to compare zero-shot
classification capability of the GPT-4 model and the GPT-3.5 model with
supervised classification performance of three model architectures: random
forests classifier, long short-term memory networks with attention (LSTM-Att),
and the UCSF-BERT model. Across all 13 tasks, the GPT-4 model performed either
significantly better than or as well as the best supervised model, the LSTM-Att
model (average macro F1 score of 0.83 vs. 0.75). On tasks with high imbalance
between labels, the differences were more prominent. Frequent sources of GPT-4
errors included inferences from multiple samples and complex task design. On
complex tasks where large annotated datasets cannot be easily collected, LLMs
can reduce the burden of large-scale data labeling. However, if the use of LLMs
is prohibitive, the use of simpler supervised models with large annotated
datasets can provide comparable results. LLMs demonstrated the potential to
speed up the execution of clinical NLP studies by reducing the need for
curating large annotated datasets. This may result in an increase in the
utilization of NLP-based variables and outcomes in observational clinical
studies.
