# Large language models are good medical coders, if provided with tools

arXiv: https://arxiv.org/abs/2407.12849 · HTML: https://mlsanity.com/paper/2407.12849/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 13 references that are in the map.
- First posted on arXiv 6 Jul 2024.

## Builds on (most cited first, 7 of 7)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction](https://mlsanity.com/paper/2112.01488/) (2021)
- [Scalable and accurate deep learning for electronic health records](https://mlsanity.com/paper/1801.07860/) (2018)
- [Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis](https://mlsanity.com/paper/1706.03446/) (2017)
- [Multimodal Machine Learning for Automated ICD Coding](https://mlsanity.com/paper/1810.13348/) (2018)

## Built on by (most cited first, 8 of 8)

- [MedDCR: Learning to Design Agentic Workflows for Medical Coding](https://mlsanity.com/paper/2511.13361/) (2025)
- [RuCCoD: Towards Automated ICD Coding in Russian](https://mlsanity.com/paper/2502.21263/) (2025)
- [Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding](https://mlsanity.com/paper/2606.13940/) (2026)
- [KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs](https://mlsanity.com/paper/2608.20887/) (2026)
- [Unlocking Historical Clinical Trial Data with ALIGN: A Compositional Large Language Model System for Medical Coding](https://mlsanity.com/paper/2411.13163/) (2024)
- [The NordDRG AI Benchmark for Large Language Models](https://mlsanity.com/paper/2506.13790/) (2025)
- [Large Language Models as Medical Codes Selectors: a benchmark using the International Classification of Primary Care](https://mlsanity.com/paper/2507.14681/) (2025)
- [Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Data](https://mlsanity.com/paper/2603.23515/) (2026)

## Abstract (arXiv)

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.