RAG-Coding: Enhancing LLM Medical Coding with Structured External Knowledge
RAG-Coding is an agentic method for automated ICD-10-CM coding that orchestrates four large language model (LLM) agents and grounds decisions in external knowledge sources, improving coding accuracy and clinical compliance. On the MDACE dataset, it outperforms the best LLM baseline by 8-13% micro-F1 and 2-8% macro-F1. Compared to PLM-ICD, RAG-Coding shows higher micro recall (+11%) but lower micro precision (-6%), with comparable F1 scores. Ablation studies confirm the importance of external knowledge. The authors also release MDACE-2025, updated with expert re-annotations based on 2025 guidelines, enabling finer-grained evaluation.
Article intelligence
Key points
- RAG-Coding uses four LLM agents and external knowledge sources to improve ICD-10-CM coding accuracy.
- On the MDACE dataset, it outperforms the best LLM baseline by 8-13% micro-F1 and 2-8% macro-F1.
- Compared to PLM-ICD, RAG-Coding achieves higher micro recall but lower micro precision, with comparable overall F1.
- The MDACE-2025 dataset is released, re-annotated with 2025 guidelines for more detailed code labels.
Why it matters
This matters because RAG-Coding uses four LLM agents and external knowledge sources to improve ICD-10-CM coding accuracy.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27377] RAG-Coding: Enhancing LLM Medical Coding with Structured External Knowledge
[Submitted on 9 Apr 2026]
Title:RAG-Coding: Enhancing LLM Medical Coding with Structured External Knowledge
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Abstract:We present RAG-Coding, an agentic method for automated ICD-10-CM coding. RAG-Coding orchestrates four large language model (LLM) agents and grounds their coding decisions in external knowledge sources (e.g. the official coding tabular list and guidelines). By retrieving and cross-referencing relevant knowledge in these sources, the agents enhance coding accuracy and ensure clinical compliance. On the MDACE dataset, RAG-Coding outperforms the best LLM-based baseline by 8-13\% in micro-F1 and 2-8\% in macro-F1 across multiple LLM backbones. Compared to the state-of-the-art pretrained language model method, PLM-ICD, RAG-Coding exhibits higher micro recall (+11\%), while PLM-ICD exhibits higher micro precision (+6\%), yielding comparable micro- and macro-F1. Ablations show stepwise gains, highlighting the importance of incorporating external knowledge. We also release MDACE-2025, updating the original dataset with expert re-annotations with the latest 2025 ICD-10-CM guidelines. This update features more fine-grained code labels and enables evaluation against current clinical standards.
Comments: Additional experiments and analyses are in progress
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
Cite as: arXiv:2605.27377 [cs.CL]
(or arXiv:2605.27377v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2605.27377
arXiv-issued DOI via DataCite
Submission history
From: Yidong Gan [view email] [v1] Thu, 9 Apr 2026 06:27:03 UTC (306 KB)
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