Veronica Preda
Papers
1
Total Citations
7
H-Index
1
About
Dr. Veronica Preda is a pioneering researcher at the intersection of artificial intelligence and surgical education, with a primary focus on developing objective, data-driven methods for assessing minimally invasive surgical (MIS) skills. Her most cited work, a 2024 narrative review, critically examines the reliability of AI-based assessment systems, specifically investigating how standardised objective metrics (SOMs) can reduce heterogeneity in skill evaluation. This contribution is particularly significant as it addresses a fundamental challenge in surgical training: the need for consistent, unbiased performance measurement. By systematically analyzing existing studies, Preda has helped establish a framework for integrating AI into surgical assessment, potentially transforming how surgeons are trained and credentialed. Her research has already garnered attention within the medical AI community, with her flagship paper accumulating 7 citations shortly after publication. Preda’s work stands out for its methodological rigor and practical implications, offering a pathway toward more reliable, automated evaluation of surgical proficiency. Her contributions are especially relevant for researchers and educators seeking to leverage AI to enhance surgical training outcomes and patient safety.
Research Focus
Key Achievements
Top Papers
- 1