Improving Clinical NLP Performance through Language Model-Generated Synthetic Clinical Data

A common challenge for the development of clinical natural language processing (NLP) methods is the availability of large annotated datasets for model training, fine-tuning, and evaluation. Traditional annotation processes are time-consuming, expensive, and often require expert medical knowledge, creating significant research and benchmark development constraints.