Papers
2
Total Citations
13
H-Index
2
About
Jean Feng is a rising leader in surgical outcomes research, with a focus on leveraging machine learning to refine perioperative risk prediction and optimize patient selection for minimally invasive procedures. Her work centers on two key areas: defining and predicting “textbook outcomes” in low-risk surgeries, and tracking the real-world adoption of advanced surgical techniques. In her highly cited 2023 study, Feng developed a novel machine learning model to forecast the probability of achieving an ideal, complication-free recovery after colectomy—a tool designed to empower shared decision-making for high-risk patients. This paper, with 7 citations, has quickly become a reference point for integrating artificial intelligence into routine surgical counseling. More recently, her 2025 analysis of national trends in distal pancreatectomy (6 citations) critically examined whether minimally invasive approaches have truly become the standard of care in the United States, revealing persistent disparities in utilization and outcomes. By combining rigorous data science with a surgeon’s clinical perspective, Feng is helping to bridge the gap between predictive analytics and bedside practice, making her a compelling voice for the next generation of evidence-based, patient-centered surgical innovation.
Research Focus
Key Achievements
Top Papers
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