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.

MedVH: Toward Systematic Evaluation of Hallucination for Large Vision Language Models in the Medical Context.

Large vision language models (LVLMs) have achieved superior performance on natural image and text tasks, inspiring extensive fine-tuning research. However, their robustness against hallucination in clinical contexts remains understudied. We propose the Medical Visual Hallucination Test (MedVH), a novel evaluation framework assessing hallucination tendencies in both medical-specific and general-purpose LVLMs.