Can Post-Training Turn LLMs into Good Medical Coders? An Empirical Study of Generative ICD Coding
Automated International Classification of Diseases (ICD) coding is a core medical-coding task for billing, epidemiology, and clinical decision support. Generative large language models (LLMs) are often reported as weak medical coders, but this finding mainly comes from inference-time settings such as prompting, retrieval, reranking, or tool use, leaving the role of task-specific post-training underexplored. We present a controlled empirical study of post-training for generative ICD coding, comparing discriminative baselines with LLM coders across prompting, supervised fine-tuning, and reinforcement learning under a common protocol and metric set. To our knowledge, this is the first study ...
| Authors | Ziqing Wang, Weihao Li, Shijie Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 June 2026 |
| Last updated at source | 11 June 2026 |
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