arXiv:2605.27377v1 Announce Type: cross 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. Compa
The proliferation of advanced large language models (LLMs) and increasing demand for efficiency in healthcare administration are converging to enable sophisticated AI agentic systems.
This development signals a significant step towards automating complex, knowledge-intensive white-collar tasks, particularly in highly regulated sectors like healthcare, demonstrating the growing capability of AI agents.
The accuracy and reliability of AI-driven medical coding are substantially improved through agentic orchestration and grounding in external knowledge, reducing human effort and errors.
- · Healthcare Providers
- · Insurers
- · LLM Providers
- · AI Agent Developers
- · Traditional Medical Coders
- · Manual Coding Software
Automated medical coding accuracy significantly improves, leading to faster claims processing and reduced administrative burden.
The precedent set by RAG-Coding could accelerate the adoption of similar agentic LLM solutions across other complex, knowledge-intensive industries.
Increased reliance on AI agents for critical administrative functions may necessitate new regulatory frameworks for AI accountability and validation in healthcare.
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