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A new multimodal deep learning model accurately predicts cancer treatment response from incomplete patient data, even when key clinical information is missing.
Injecting retrieved anatomical priors into text-to-CT generation dramatically improves image fidelity and clinical consistency, offering a scalable path to more realistic medical image synthesis.
