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Quantum-enhanced neural networks can forecast patient vitals with accuracy rivalling classical methods, while showing greater resilience to noisy or incomplete data.
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.
Radiology report generation can be both more accurate AND more interpretable: CEMRAG uses visual concepts to enhance multimodal RAG, challenging the assumed trade-off between transparency and diagnostic accuracy.
