RAG-Coding: Enhancing LLM Medical Coding with Structured External Knowledge
Researchers have developed RAG-Coding, a novel method that uses four large language model (LLM) agents to improve the accuracy of automated medical coding for ICD-10-CM. This approach grounds the LLMs' decisions in external knowledge sources like official coding guidelines and tabular lists, enhancing clinical compliance. In evaluations on the MDACE dataset, RAG-Coding demonstrated significant improvements over existing LLM-based baselines, achieving higher micro-F1 and macro-F1 scores. The study also introduced an updated dataset, MDACE-2025, which incorporates the latest 2025 ICD-10-CM guidelines for more precise evaluation. AI
IMPACT This research could lead to more accurate and compliant automated medical coding systems, reducing errors and improving healthcare administration.