K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model
arXiv:2509.25594v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to individual tasks, modalities, or organs. This fragmentation contrasts sharply with clinical practice, where experts seamlessly integrate diverse knowledge: anatomical priors from training, exemplar-based reasoning from reference cases, and iterative refinement through real-time interaction. We present $\textbf{K-Prism}$, a unified segmentation framework that mi
The proliferation of specialized AI models in medical imaging has created fragmentation, indicating a maturing field ready for unification through more generalized approaches.
A unified medical image segmentation model like K-Prism could significantly accelerate clinical diagnostic workflows and reduce diagnostic errors by integrating diverse knowledge sources.
Current fragmented medical AI models, specific to individual tasks or modalities, are challenged by a new architecture that integrates anatomical priors, exemplar reasoning, and iterative refinement.
- · Medical AI developers
- · Healthcare providers
- · Patients
- · Medical device manufacturers
- · Fragmented, single-task medical AI solutions
- · Conventional diagnostic methods reliant on manual interpretation
Improved diagnostic accuracy and efficiency in medical imaging.
Reduced healthcare costs through more automated and precise medical analysis at scale.
Accelerated development of personalized medicine due to better foundational data from unified AI interpretation.
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Read at arXiv cs.AI