# KREL: Automatic Medical Coding via Knowledge-Guided Reasoning over Clinical Evidence with LLMs

arXiv: https://arxiv.org/abs/2608.20887 · HTML: https://mlsanity.com/paper/2608.20887/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 13 of its 38 references that are in the map.
- First posted on arXiv 21 Aug 2026.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 13 of 13)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models](https://mlsanity.com/paper/2506.05176/) (2025)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [A Label Attention Model for ICD Coding from Clinical Text](https://mlsanity.com/paper/2007.06351/) (2020)
- [Towards Agentic Recommender Systems in the Era of Multimodal Large Language Models](https://mlsanity.com/paper/2503.16734/) (2025)
- [Automated clinical coding using off-the-shelf large language models](https://mlsanity.com/paper/2310.06552/) (2023)
- [Exploring LLM Multi-Agents for ICD Coding](https://mlsanity.com/paper/2406.15363/) (2024)
- [Large language models are good medical coders, if provided with tools](https://mlsanity.com/paper/2407.12849/) (2024)
- [MedDCR: Learning to Design Agentic Workflows for Medical Coding](https://mlsanity.com/paper/2511.13361/) (2025)
- [Evaluating Hierarchical Clinical Document Classification Using Reasoning-Based LLMs](https://mlsanity.com/paper/2507.03001/) (2025)
- [Seeing and Reflecting: Multimodal Memory-Enhanced Agent Collaboration for Recommendation](https://mlsanity.com/paper/2607.07108/) (2026)

## Built on by (most cited first, 1 of 1)

- [Beyond ICD Codes: Semantic IDs for Generative Medical Coding](https://mlsanity.com/paper/2610.19301/) (2026)

## Abstract (arXiv)

Automatic Medical Coding (AMC), which assigns standardized International Classification of Diseases (ICD) codes to clinical notes, is essential for medical reimbursement, quality reporting, and clinical research. Existing pre-trained language model (PLM)-based methods typically formulate AMC as an extreme multi-label classification problem over a predefined code set, while recent large language model (LLM)-based approaches instead frame it as generation or multi-step reasoning. However, key challenges remain, including the extreme length of clinical notes that hinders effective interpretation, the vast ICD label space, and complex coding rules that are not explicitly captured by LLMs. In this work, we propose Knowledge-Guided Reasoning over Clinical Evidence with LLMs (KREL), a framework that leverages LLMs for clinical text understanding and reasoning while integrating external ICD coding guidelines as structured knowledge. This design enables tight coupling between domain knowledge and LLM reasoning, reducing hallucinations and improving compliance with coding standards. Experiments on benchmark datasets show that KREL consistently outperforms strong PLM-based and state-of-the-art LLM-based baselines.