Abstract
The adoption of large language models (LLMs) in healthcare has garnered significant research interest, yet their performance remains limited due to a lack of domain-specific knowledge, medical reasoning skills, and their unimodal nature, which restricts them to text-only inputs. To address these limitations, we propose MultiMedRes, a multimodal medical collaborative reasoning framework that simulates human physicians' communication by incorporating a learner agent to proactively acquire information from domain-specific expert models. MultiMedRes addresses medical multimodal reasoning problems through three steps i) Inquire: The learner agent decomposes complex medical reasoning problems into multiple domain-specific sub-problems; ii) Interact: The agent engages in iterative "ask-answer" interactions with expert models to obtain domain-specific knowledge; and iii) Integrate: The agent integrates all the acquired domain-specific knowledge to address the medical reasoning problems (e.g., identifying the difference of disease levels and abnormality sizes between medical images). We validate the effectiveness of our method on the task of difference visual question answering for X-ray images. The experiments show that our zero-shot prediction achieves state-of-the-art performance, surpassing fully supervised methods, which demonstrates that MultiMedRes could offer trustworthy and interpretable assistance to physicians in monitoring the treatment progression of patients, paving the way for effective human-AI interaction and collaboration.