用动态图结构增强医疗多模态推理，支持诊断过程回溯与修正。
Agentic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare
- 构建时序图结构，支持诊断推理的回溯与动态调整。
- 融合多模态数据在不同时间点的信息，追踪病情发展。
- 多智能体框架提升诊断准确性，适合临床辅助决策场景。
医疗与医学是处理多模态数据以进行复杂推理和疾病诊断的多模态领域。尽管已有部分多模态推理模型用于科学领域的复杂任务，但在医疗领域应用仍有限，且在正确诊断推理方面表现不足。为此，本文提出一种基于有向图的时序图式推理模型，通过回溯、重构推理内容以及新增或删除推理节点，动态适应变化，以获得最优推荐或答案。该模型能整合不同时间点的多模态数据，实现对患者健康状况和疾病进展的追踪分析。此外，所提出的多智能体时序推理框架具备任务分配与交叉验证机制，进一步提升推理输出的准确性。初步实验与分析验证了该方法的新颖性与实际应用价值。
原文摘要 · Abstract (English)
Healthcare and medicine are multimodal disciplines that deal with multimodal data for reasoning and diagnosing multiple diseases. Although some multimodal reasoning models have emerged for reasoning complex tasks in scientific domains, their applications in the healthcare domain remain limited and fall short in correct reasoning for diagnosis. To address the challenges of multimodal medical reasoning for correct diagnosis and assist the healthcare professionals, a novel temporal graph-based reasoning process modelled through a directed graph has been proposed in the current work. It helps in accommodating dynamic changes in reasons through backtracking, refining the reasoning content, and creating new or deleting existing reasons to reach the best recommendation or answer. Again, consideration of multimodal data at different time points can enable tracking and analysis of patient health and disease progression. Moreover, the proposed multi-agent temporal reasoning framework provides task distributions and a cross-validation mechanism to further enhance the accuracy of reasoning outputs. A few basic experiments and analysis results justify the novelty and practical utility of the proposed preliminary approach.
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