Guanqi Hang

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About

Guanqi Hang is a rising researcher at the intersection of artificial intelligence and interventional radiology, with a primary focus on AI-guided medical imaging and surgical planning. His most notable contribution is the development of an AI-powered segmentation and path planning software for transthoracic lung biopsy, published in 2024, which has already garnered 14 citations—a strong early impact indicator for a young researcher. This work addresses a critical clinical challenge: improving the accuracy and safety of needle-based lung biopsies by automating the identification of optimal needle trajectories and avoiding critical structures like blood vessels and airways. By integrating deep learning segmentation with path optimization algorithms, Hang’s approach reduces procedure time and potential complications, offering a tangible step toward autonomous or semi-autonomous biopsy systems. His research bridges computer vision, robotics, and clinical workflow, demonstrating a clear translational vision. While still early in his career, Guanqi Hang’s work signals a promising trajectory in AI-assisted minimally invasive procedures, with potential to reshape how radiologists plan and execute lung biopsies.