Improving Clinical NLP Performance through Language Model-Generated
  Synthetic Clinical Data
Shan Chen Jack Gallifant Marco Guevara Yanjun Gao Majid Afshar Timothy Miller Dmitriy Dligach Danielle S. Bitterman
Generative models have been showing potential for producing data in mass.
This study explores the enhancement of clinical natural language processing
performance by utilizing synthetic data generated from advanced language
models. Promising results show feasible applications in such a high-stakes
domain.