Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space
Abstrak
Visual spatial intelligence is critical for medical image interpretation, yet remains largely unexplored in Multimodal Large Language Models (MLLMs) for 3D imaging. This gap persists due to a systemic lack of datasets featuring structured 3D spatial annotations beyond basic labels. In this study, we introduce an agentic pipeline that autonomously synthesizes spatial visual question-answering (VQA) data by orchestrating computational tools such as volume and distance calculators with multi-agent collaboration and expert radiologist validation. We present SpatialMed, the first comprehensive benchmark for evaluating 3D spatial intelligence in medical MLLMs, comprising nearly 10K question-answer pairs across multiple organs and tumor types. Our evaluations on 14 state-of-the-art MLLMs and extensive analyses reveal that current models lack robust spatial reasoning capabilities for medical imaging.
Topik & Kata Kunci
Penulis (10)
Quoc-Huy Trinh
Xi Ding
Yang Liu
Zhenyue Qin
Xingjian Li
Gorkem Durak
Halil Ertugrul Aktas
Elif Keles
Ulas Bagci
Min Xu
Akses Cepat
- Tahun Terbit
- 2026
- Bahasa
- en
- Sumber Database
- arXiv
- Akses
- Open Access ✓