Inquire, Interact, and Integrate: A Proactive Agent Collaborative Framework for Zero-Shot Multimodal Medical Reasoning

• URL: http://arxiv.org/abs/2405.11640v1
• Date: Sun, 19 May 2024 18:26:11 GMT
• Title: Inquire, Interact, and Integrate: A Proactive Agent Collaborative Framework for Zero-Shot Multimodal Medical Reasoning
• Authors: Zishan Gu, Fenglin Liu, Changchang Yin, Ping Zhang,
• Abstract summary: The adoption of large language models (LLMs) in healthcare has attracted significant research interest. Most state-of-the-art LLMs are unimodal, text-only models that cannot directly process multimodal inputs. We propose a multimodal medical collaborative reasoning framework textbfMultiMedRes to solve medical multimodal reasoning problems.
• Score: 21.562034852024272
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The adoption of large language models (LLMs) in healthcare has attracted significant research interest. However, their performance in healthcare remains under-investigated and potentially limited, due to i) they lack rich domain-specific knowledge and medical reasoning skills; and ii) most state-of-the-art LLMs are unimodal, text-only models that cannot directly process multimodal inputs. To this end, we propose a multimodal medical collaborative reasoning framework \textbf{MultiMedRes}, which incorporates a learner agent to proactively gain essential information from domain-specific expert models, to solve medical multimodal reasoning problems. Our method includes three steps: i) \textbf{Inquire}: The learner agent first decomposes given complex medical reasoning problems into multiple domain-specific sub-problems; ii) \textbf{Interact}: The agent then interacts with domain-specific expert models by repeating the ``ask-answer'' process to progressively obtain different domain-specific knowledge; iii) \textbf{Integrate}: The agent finally integrates all the acquired domain-specific knowledge to accurately address the medical reasoning problem. We validate the effectiveness of our method on the task of difference visual question answering for X-ray images. The experiments demonstrate that our zero-shot prediction achieves state-of-the-art performance, and even outperforms the fully supervised methods. Besides, our approach can be incorporated into various LLMs and multimodal LLMs to significantly boost their performance.

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