Agentic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare
This work addresses the problem of improving diagnostic accuracy for healthcare professionals, though it appears incremental as it builds on existing multimodal reasoning models.
The paper tackles the challenge of multimodal medical reasoning for accurate diagnosis by proposing a temporal graph-based reasoning process that accommodates dynamic changes through backtracking and refinement, with preliminary experiments justifying its novelty and utility.
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.