Efficient Medical VIE via Reinforcement Learning
Lijun Liu, Ruiyang Li, Zhaocheng Liu, Chenglin Zhu, Chong Li, Jiehan Cheng, Qiang Ju, Jian Xie
2025-06-18
Summary
This paper talks about using a method called reinforcement learning with verifiable rewards to improve a large language model called Qwen2.5-VL-7B in a medical task known as visual information extraction, or VIE, which means getting important details from medical images and data.
What's the problem?
The problem is that getting accurate information from medical images is really hard, especially when there are only a few examples to learn from. Also, models have to be good at reasoning and be balanced between being precise and not missing important details.
What's the solution?
The researchers used reinforcement learning with verifiable rewards to train the Qwen2.5-VL-7B model, even with limited labeled medical data. This method helped the model get better at understanding and extracting medical information visually while balancing accuracy and completeness.
Why it matters?
This matters because improving how AI works with medical images can help doctors and healthcare professionals get faster and more accurate information, which can lead to better patient care and outcomes.
Abstract
An RLVR framework using fine-tuned Qwen2.5-VL-7B achieves state-of-the-art performance in medical VIE with limited annotated samples, enhancing reasoning and balance between precision and recall.