Assessing Medical Diagnosis with Large Language Models: Limitations of Rouge Metrics and Future Directions

The authors explain that medical diagnosis is different from other areas of NLP in that there is often no single gold standard diagnosis, and different doctors may provide different diagnostic opinions based on their experiences and judgments. They suggest that incorporating multiple reference standards would allow for a more comprehensive evaluation of the quality of medical diagnosis.
The authors also discuss the potential of using large language models (LLMs) to generate the diagnostic section of medical reports, which could save time and effort for healthcare professionals. However, they acknowledge that there are challenges in applying LLMs to this task, including the need for high-quality training data and the potential for bias in the models.
Overall, the article emphasizes the importance of developing more sophisticated metrics for evaluating the quality of medical diagnosis and explores the use of LLMs as a potential tool for improving the efficiency and consistency of medical reports.