Local Large Language Models for Complex Structured Medical Tasks

Understand

This paper introduces an approach that combines the language reasoning capabilities of large language models (LLMs) with the benefits of local training to tackle complex, domain-specific tasks.

• Specifically, the authors demonstrate their approach by extracting structured condition codes from pathology reports.
• The proposed approach utilizes local LLMs, which can be fine-tuned to respond to specific generative instructions and provide structured outputs.
• The authors collected a dataset of over 150k uncurated surgical pathology reports, containing gross descriptions, final diagnoses, and condition codes.