Vaibhavi Shah
Papers
2
Total Citations
11
H-Index
2
About
Vaibhavi Shah is a rising researcher at the intersection of machine learning and neurosurgery, with a primary focus on developing predictive and generalizable AI tools for spinal and cranial interventions. Her major contributions include a comprehensive 2024 review that maps how machine learning can transform neurosurgery—from reconstructing medical images and predicting surgical outcomes from video to enabling robotic navigation and intraoperative decision-making. This work, already garnering 8 citations, positions her as a key voice in translating complex AI inputs into actionable clinical predictions. In a separate 2023 study, she evaluated the accuracy of the Mazor X-Align™ robotic system in predicting postoperative segmental lumbar lordosis, a critical parameter for successful spinal fusion. Her findings provide early evidence on the reliability of preoperative planning software, addressing a gap in personalized spine surgery. With a growing citation footprint and a focus on generalizable, real-world applications, Shah’s work bridges computational innovation and surgical precision—offering a roadmap for the next generation of AI-assisted neurosurgery.
Research Focus
Key Achievements
Top Papers
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