Reinforcement Learning Improves LLM Accuracy and Reasoning in Disease Classification from Radiology Reports
On the map: LLM reasoning → Clinical and biomedical applications
Abstract (arXiv)
Accurate disease classification from radiology reports is essential for many applications. While supervised fine-tuning (SFT) of lightweight LLMs improves accuracy, it can degrade reasoning. We propose a two-stage approach: SFT on disease labels followed by Group Relative Policy Optimization (GRPO) to refine predictions by optimizing accuracy and format without reasoning supervision. Across three radiologist-annotated datasets, SFT outperformed baselines and GRPO further improved classification and enhanced reasoning recall and comprehensiveness.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning. Builds on 21 of its 30 references that are in the map. First posted on arXiv 21 Apr 2026.
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- 2019MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographscited by 597
- 2021RadGraph: Extracting Clinical Entities and Relations from Radiology Reportscited by 319
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- 2023BioInstruct: Instruction Tuning of Large Language Models for Biomedical Natural Language Processingcited by 25
- 2025The Rise of Small Language Models in Healthcare: A Comprehensive Surveycited by 18
- 2024Enhancing disease detection in radiology reports through fine-tuning lightweight LLM on weak labelscited by 5
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Citing papers per year: 2026: 2.
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- 2025OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation
- 2026Enhancing Visual Reasoning in Chest X-Ray Report Generation Using Reinforcement Learning
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- 2025Reason Like a Radiologist: Chain-of-Thought and Reinforcement Learning for Verifiable Report Generation