This research explores how expert guidance and specific data improve the performance of large language models in oncology.
Leveraged expert feedback to enhance model training.
Incorporated domain-specific data for clinical applications.
Evaluated the performance of LLMs in decision-making scenarios.
Expert-guided LLMs showed improved accuracy in clinical decision support.
Significant performance gains were observed compared to standard models.
Integration of LLMs into clinical workflows was suggested as a future research direction.
Abstract
Expert feedback and domain-specific data augment LLM performance. Future research should investigate responsible LLM integration into real-world clinical workflows.