Ophiuchus: Incentivizing Tool-augmented "Think with Images" for Joint Medical Segmentation, Understanding and Reasoning
arXiv:2512.14157v2 Announce Type: replace Abstract: Recent medical MLLMs have made significant progress in generating step-by-step textual reasoning chains. However, they still struggle with complex clinical tasks that necessitate dynamic and iterative focusing on fine-grained visual regions. To close this gap, we introduce Ophiuchus, a versatile, tool-augmented framework that equips an MLLM to (i) decide when fine-grained visual evidence is needed, (ii) determine where to probe and ground within the medical image, and (iii) seamlessly weave the relevant sub-image content back into an interlea
The continuous evolution of MLLMs necessitates improved handling of complex, fine-grained medical imagery, pushing for more sophisticated systems like Ophiuchus.
This development represents a significant step towards more reliable and autonomous AI in medical diagnostics, enhancing accuracy and reducing manual review burdens.
MLLMs are moving beyond simple textual reasoning to integrate dynamic visual probing and context re-integration, making them more adept at complex tasks.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Medical imaging companies
- · AI models lacking visual grounding
- · Manual diagnostic workflows
More accurate and efficient medical image analysis becomes possible through tool-augmented MLLMs.
The improved diagnostic capabilities could lead to earlier disease detection and more personalized treatment plans.
This technology might eventually enable fully autonomous AI diagnostic systems, shifting the role of human specialists towards oversight and complex case management.
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Read at arXiv cs.AI