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3DReasonKnee: Advancing Grounded Reasoning in Medical Vision Language Models
Sraavya Sambara1, Sung Eun Kim2, Xiaoman Zhang1
1Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
Summary
This study introduces 3DReasonKnee, a novel dataset for 3D medical imaging, enabling vision-language models (VLMs) to perform grounded reasoning for enhanced diagnostic accuracy. It benchmarks VLM performance in localizing anatomical regions and assessing severity in knee MRIs.
Area of Science:
- Medical Imaging AI
- Computer Vision
- Clinical Decision Support
Background:
- Current vision-language models (VLMs) lack the ability to ground anatomical regions in 3D medical images and perform step-by-step reasoning, hindering clinical adoption.
- Existing 3D datasets do not support the
- grounded reasoning
- required for realistic diagnostic workflows and trustworthy clinician-AI collaboration.
Purpose of the Study:
- To introduce 3DReasonKnee, the first dataset enabling 3D grounded reasoning for medical images.
- To facilitate the development of VLMs capable of localized, step-by-step diagnostic assessment in 3D medical volumes.
- To establish a benchmark for evaluating VLM performance in anatomical localization and diagnostic reasoning.
Main Methods:
- Developed 3DReasonKnee, a dataset comprising 7,970 3D knee MRI volumes and 494,000 quintuples.
- Each quintuple includes MRI volume, diagnostic question, 3D bounding box, clinician-generated reasoning steps, and severity assessment.
- Created ReasonKnee-Bench for evaluating VLM localization and diagnostic accuracy, benchmarking five state-of-the-art VLMs.
Main Results:
- Established a novel benchmark (ReasonKnee-Bench) for evaluating 3D grounded reasoning in medical VLMs.
- Provided baseline performance metrics for five leading VLMs on localization and diagnostic accuracy tasks.
- Demonstrated the potential for improved VLM performance in clinically relevant 3D medical image analysis.
Conclusions:
- 3DReasonKnee is a unique resource for advancing multimodal medical AI, capturing orthopedic surgeons' diagnostic expertise.
- The dataset and benchmark facilitate the development of AI systems capable of 3D, clinically aligned, localized decision-making.
- Future work can leverage 3DReasonKnee to enhance clinician-AI collaboration and diagnostic trust.
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