A Proactive Agent Collaborative Framework for Zero-Shot Multimodal Medical Reasoning.

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.