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Real-time, marker-free tracking of surgical robots now achieves near-1 cm accuracy at 30 fps, outpacing traditional methods even in occluded environments.
Explicitly modeling annotator bias and variability can significantly enhance uncertainty calibration in medical image segmentation without sacrificing accuracy.
Standardized testbeds and effectiveness metrics could accelerate the development and validation of AI-assisted robotic thrombectomy, potentially revolutionizing stroke treatment accessibility.
