Antonia Stern
Papers
1
Total Citations
16
H-Index
1
About
Dr. Antonia Stern is a pioneering researcher at the intersection of surgery and artificial intelligence, with a primary focus on surgomics—the extraction of quantitative, machine-learning-driven features from intraoperative data to predict patient outcomes. Her major contribution lies in developing active learning frameworks that dramatically reduce the annotation burden on medical experts while maintaining high-quality data for training predictive models. In her landmark 2023 study, "Active learning for extracting surgomic features in robot-assisted minimally invasive esophagectomy," she demonstrated how intelligent sampling strategies can prioritize the most informative surgical process characteristics for expert review, achieving robust model performance with a fraction of the usual labeled data. This work, already garnering 16 citations in a rapidly evolving field, positions her at the forefront of personalized surgical analytics. Dr. Stern’s research promises to transform how surgeons leverage multimodal data—from video to instrument kinematics—to forecast complications and tailor interventions in real time, making her a key voice in the emerging discipline of data-driven precision surgery.
Research Focus
Key Achievements
Top Papers
- 1