Enhancing Step-by-Step and Verifiable Medical Reasoning in MLLMs
Haoran Sun, Yankai Jiang, Wenjie Lou, Yujie Zhang, Wenjie Li, Lilong Wang, Mianxin Liu, Lei Liu, Xiaosong Wang
2025-06-24
Summary
This paper talks about MICS, a new method that improves large medical language models by helping them think through medical problems step-by-step and answer questions about images with better reasoning.
What's the problem?
The problem is that medical AI often struggles to perform reliable and understandable reasoning when dealing with complex medical questions and visuals, which is important for trust and accuracy.
What's the solution?
The researchers developed a reasoning-path searching scheme that guides the model to generate detailed chains of thought using carefully created data. This approach helps the model reason more robustly and generalize better to different medical tasks.
Why it matters?
This matters because it helps medical AI models become more trustworthy and useful in clinical settings, improving diagnosis and decision-making by giving clearer and more accurate explanations.
Abstract
MICS, a novel reasoning-path searching scheme, enhances medical MLLMs like Chiron-o1 with robust generalizable reasoning and visual question-answering capabilities through comprehensive chain-of-thought data generation.