ClinFusion: A Vision-Centric Multimodal LLM System for Holistic Medical Understanding
Score
79论文提出了统一2D/3D医学图像的编码架构和新型评估方法，信息增量显著。
实验设计全面，包含多个基准和放射科医生盲评，论述严谨。
模型可直接应用于临床报告生成和辅助诊断，具有较高实用潜力。
论文为最新预印本，紧跟多模态大模型在医疗领域的前沿。
来源为arXiv预印本，未经同行评审，但作者及实验方法可信。
Base 88 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
The paper presents breakthroughs in both architecture design and evaluation methodology, with comprehensive experiments and blinded validation, marking an important advancement in medical AI.
AI commentary
「This paper finds a sweet spot in medical multimodal LLMs with its vision-centric design. The inclusion of blinded radiologist evaluation adds credibility, making it especially valuable for practitioners focused on clinical AI deployment and evaluation paradigms. Researchers should pay attention to the cascaded encoder design and the ROI-based evaluation metric.」
Quick overview
ClinFusion is a vision-centric multimodal large language model designed to tackle heterogeneity in medical image understanding and evaluation alignment. It proposes a cascaded spatial-aware locality fusion encoder to unify 2D and 3D medical images, and introduces MedIF-Bench for instruction-following and a region-of-interest-grounded metric for report generation evaluation. Across 24 benchmarks covering VQA, report generation, and instruction-following, ClinFusion outperforms open-source medical MLLMs (e.g., Hulu-Med, Lingshu) on 20, and surpasses proprietary models like GPT-5.2 and Gemini-3-Flash on 13 out of 16. A blinded evaluation by board-certified radiologists confirms ClinFusion produces the highest-quality reports, and its RoI-grounded metric achieves the strongest correlation with expert judgment. It can be further augmented with agentic tool use for retrieval-augmented and tool-assisted clinical workflows.
- ClinFusion uses a cascaded spatial-aware locality fusion encoder to unify 2D/3D medical image understanding.
- It introduces MedIF-Bench for instruction-following and a region-of-interest-grounded metric for report evaluation.
- Outperforms open-source medical MLLMs on 20/24 benchmarks and proprietary models (GPT-5.2, Gemini-3-Flash) on 13/16 benchmarks.
- Blinded radiologist evaluation confirms ClinFusion's reports are highest-ranked; new metric best correlates with expert judgment.
Full text
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