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Scaling clinical LLMs doesn't guarantee safety: high-risk errors persist even with advanced RAG and max-context prompting, highlighting the critical role of evidence quality and deployment strategy.
Achieve state-of-the-art 3D medical image generation by reformulating deterministic prediction as a multi-objective drifting problem, outperforming GANs, flow-matching, and SDEs in fidelity, realism, and efficiency.
Achieve state-of-the-art zero-shot chest X-ray classification by cleverly curating training data and distilling knowledge into CLIP models, sidestepping the need for large-scale retraining.
