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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 mirrors this clinical flexibility by systematically integrating three knowledge paradigms: (i) $\\textit{semantic priors}$ learned from annotated datasets, (ii) $\\textit{in-context knowledge}$ from few-shot reference examples, and (iii) $\\textit{interactive feedback}$ from user inputs like clicks or scribbles. 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