Xiaofeng Liu

Early evidence suggests that medical world models could revolutionize patient care by enabling dynamic, intervention-responsive AI systems, but critical hurdles remain.

Current evaluation practices for agentic AI in medicine are misaligned with clinical needs, risking the reliability of these systems in real-world applications.

VCDP transforms semi-supervised medical image segmentation by aligning voxel embeddings with both global organ identities and local anatomical variations, leading to superior performance on challenging segmentation tasks.

HPR-SAM outperforms existing methods by effectively capturing complex anatomical representations, achieving state-of-the-art results in medical image segmentation without the need for prompts.

SHTA achieves significant improvements in segmentation accuracy by ensuring semantic consistency in challenging regions, all without increasing inference costs.