# Agentic Temporal Graph of Reasoning with Multimodal Language Models: A Potential AI Aid to Healthcare

arXiv: https://arxiv.org/abs/2509.11944 · HTML: https://mlsanity.com/paper/2509.11944/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 1 of its 2 references that are in the map.
- First posted on arXiv 15 Sep 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)
- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 1 of 1)

- [MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding](https://mlsanity.com/paper/2501.18362/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond Component Testing: Validating Agentic AI Systems](https://mlsanity.com/paper/2607.29405/) (2026)

## Abstract (arXiv)

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.