MedVA: An End-to-End Neuro-Symbolic Agentic System for Medical Volume Visualization
Medical volume visualization requires selecting regions of interest (ROIs) and carefully controlling their relative visual emphasis according to a given clinical intent. Implementing these decisions in conventional workflows demands substantial clinical and visualization expertise and often involves trial-and-error optimization. Recent agentic systems have introduced natural-language interaction and autonomous visualization operations but largely rely on MLLM-based inference throughout the workflow. Although MLLMs encode broad medical knowledge and provide strong reasoning capabilities, such inference may be suboptimal for medical volume visualization, potentially leading to clinically in...
| Authors | Haill An, Suhyeon Kim, Minjun Kang et al. |
|---|---|
| Primary category | cs.GR |
| Published | 14 September 2026 |
| Last updated at source | 14 September 2026 |
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