AI Applications for Quality Assurance and Decision Support in Interventional Medicine

PICR DDMS Seminar

Title: AI Applications for Quality Assurance and Decision Support in Interventional Medicine

Speaker: Fiona Kolbinger

Abstract: Artificial Intelligence (AI) tools have demonstrated expert-level performance across many clinical tasks, such as breast cancer detection on mammograms or skin cancer diagnosis. Recent clinical trials provide evidence that AI tools will be an integral part of future standards of care. However, in surgery, the dynamic and complex nature of the intraoperative environment presents unique challenges for AI integration. Unlike static diagnostic imaging, surgical decision support requires real-time processing of high-dimensional, multi-modal data (e.g., surgical videos, medical imaging, and electronic health records) to provide guidance and predictive insights.

Furthermore, the successful integration of AI into clinical care hinges on rigorous, transparent, and reproducible validation. In addition to technical reproducibility, real-world patient care scenarios demand broader validation strategies, many of which are shaped by regulatory frameworks and clinical realities. This presentation will provide an overview of AI applications in interventional medicine, as well as the translational pathway from retrospective model development on curated datasets to clinical deployment in surgical AI applications. It will highlight key validation challenges along this pathway and present interdisciplinary approaches to address them, emphasizing the importance of aligning technical, clinical, and regulatory perspectives to ensure safe and impactful AI adoption in surgery.

Bio: Fiona Kolbinger is a Research Assistant Professor of Biomedical Engineering at Purdue University. She completed her medical training at the University of Heidelberg School of Medicine in Germany and holds a research doctorate (Dr. med.) in Precision Oncology from the German Cancer Research Center, as well as a Ph.D. in Surgical Data Science from Dresden University of Technology in Dresden, Germany. With clinical training in general surgery and a decade of interdisciplinary research experience, her research focuses on the development and clinical translation of data-driven tools for clinical decision support. Specifically, her work focuses on AI-based intraoperative guidance and surgical quality control, as well as complication risk and outcome prediction in surgery and interventional medicine. Dr. Kolbinger’s research ultimately aims to improve patient outcomes by enabling more personalized, data-informed, and interdisciplinary approaches to surgical care.

Thursday, May 7, 2026 – 11:00 AM
Hall for Discovery Learning and Research (DLR) Room 131