Sarah Shi
Papers
2
Total Citations
22
H-Index
2
About
Sarah Shi is a rising leader in orthopedic data science, whose work is redefining how machine learning and real-world evidence can improve surgical outcomes in robotic-assisted total knee arthroplasty (RATKA). Her research centers on the intersection of artificial intelligence, surgical efficiency, and patient-specific factors, with a focus on leveraging large clinical datasets to optimize operative workflows and enhance recovery. In her landmark 2023 study, Shi demonstrated how machine learning models can predict operative time in RATKA by analyzing patient demographics, surgeon behavior, and procedural variables—a contribution that earned 16 citations and offers a practical tool for improving operating room utilization. Her 2022 cluster analysis of 853 total knee arthroplasty patients further advanced the field by identifying how patient and procedural factors collectively influence physical outcomes, providing a data-driven framework for personalized surgical planning. Though early in her career, Shi’s work is already shaping a more efficient, evidence-based approach to joint replacement, positioning her as a key voice in the movement toward smarter, data-informed orthopedics.
Research Focus
Key Achievements
Top Papers
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