The Gap Between LLMs and Expert Biomedical Annotation
Why frontier language models struggle with specialized biomedical text mining
This research identifies critical limitations of large language models when applied to specialized biomedical text annotation tasks.
- Dataset-specific nuances - LLMs fail to learn implicit rules that human annotators absorb through training
- Formatting challenges - Standard LLM prompting approaches often conflict with biomedical annotation requirements
- Domain complexity - Biomedical text contains specialized terminology and relationships that general-purpose LLMs struggle to process
For medical applications, these findings highlight the continuing need for expert human annotation in critical biomedical text mining workflows, while suggesting specific areas where LLM capabilities can be enhanced.