arXiv:2606.11740v1 Announce Type: cross Abstract: We study whether grounded reasoning supervision from abundant 2D medical images can improve 3D medical VQA when both input types are aligned through a common reasoning interface. We introduce UniReason-Med, a single-checkpoint framework that processes either a 2D image or a slice-serialized 3D volume at inference time, generating interleaved textual reasoning and localized visual evidence through shared box syntax, region-token injection, and a common grounded reasoning policy. To train this interface, we construct UniMed-CoT, a 220K instructio
The rapid advancement in multimodal AI and the increasing availability of detailed medical imaging datasets are converging, making a unified 2D/3D approach to medical VQA both feasible and necessary.
This work represents a significant step towards more generalized and robust AI diagnostics, potentially standardizing the interface for analyzing complex medical data regardless of its initial dimensionality.
AI models can now learn and apply reasoning across both 2D and 3D medical images using a single framework, improving diagnostic accuracy and efficiency by leveraging broader data sources.
- · Medical AI developers
- · Radiology departments
- · Healthcare technology companies
- · Patients needing advanced diagnostics
- · Niche 2D-only medical imaging AI solutions
- · Manual or less integrated diagnostic workflows
Improved diagnostic accuracy and throughput in medical imaging analysis.
Reduced physician workload and faster identification of complex medical conditions.
Accelerated drug discovery and treatment development through better disease characterization.
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Read at arXiv cs.CL