  This study presents a novel two-stage Retrieve-Rank system for automated
ICD-10-CM medical coding, comparing its performance against a Vanilla Large
Language Model (LLM) approach. Evaluating both systems on a dataset of 100
single-term medical conditions, the Retrieve-Rank system achieved 100% accuracy
in predicting correct ICD-10-CM codes, significantly outperforming the Vanilla
LLM (GPT-3.5-turbo), which achieved only 6% accuracy. Our analysis demonstrates
the Retrieve-Rank system's superior precision in handling various medical terms
across different specialties. While these results are promising, we acknowledge
the limitations of using simplified inputs and the need for further testing on
more complex, realistic medical cases. This research contributes to the ongoing
effort to improve the efficiency and accuracy of medical coding, highlighting
the importance of retrieval-based approaches.
