Enhancing Visual Reasoning in Chest X-Ray Report Generation Using Reinforcement Learning
Medical report generation has made significant progress with the rise of modern vision-language models and the growing availability of large-scale medical datasets. However, hallucinations remain a major challenge, largely due to the limitations of supervised fine-tuning (SFT), which prioritizes lexical similarity to reference reports rather than clinical correctness. While reinforcement learning has shown strong performance in domains with verifiable rewards such as mathematics and code generation, its application to open-ended medical tasks remains limited. Existing work focuses on evaluating final answers, overlooking the model's reasoning, despite evidence that flawed reasoning can de...
| Authors | Denis Musinguzi, Andrew Katumba, Prasenjit Mitra |
|---|---|
| Primary category | cs.CV |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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