Presented By: Department of Computational Medicine and Bioinformatics DCMB
DCMB Tools and Technology Seminar by Jingying Wang (Computer Science & Engineering)
“Human-AI Systems for Externalizing Surgical Expertise”
Join us for the DCMB Tools & Technology Seminar Series featuring a presentation by Jingying Wang from the Department of Computer Science and Engineering. Enjoy a complimentary pizza lunch while learning about innovative research and cutting-edge tools.
“Human-AI Systems for Externalizing Surgical Expertise”
Associated Link: https://wjymonica.github.io/
Abstract
Surgical expertise is largely tacit: what separates experts from novices often lies in perceptual and decision-making skills, including where to look, when it is safe to proceed, and how to perform each action. This is where human-AI systems can help. By capturing multimodal signals such as gaze, surgical video, narration, and instrument motion, they can help replay and visualize expert performance, convert surgical recordings into learning resources, and summarize experts’ mental models. In this talk, I will present three examples: SurgGaze, which captures surgeons’ attention intraoperatively; Surgment, which creates perceptual and cognitive exercises from surgical recordings; and eXplainMR, an ultrasound practice platform that provides explanations for action guidance. Together, these examples show how human-AI systems can make surgical expertise more transferable, accessible, and scalable for medical students.
“Human-AI Systems for Externalizing Surgical Expertise”
Associated Link: https://wjymonica.github.io/
Abstract
Surgical expertise is largely tacit: what separates experts from novices often lies in perceptual and decision-making skills, including where to look, when it is safe to proceed, and how to perform each action. This is where human-AI systems can help. By capturing multimodal signals such as gaze, surgical video, narration, and instrument motion, they can help replay and visualize expert performance, convert surgical recordings into learning resources, and summarize experts’ mental models. In this talk, I will present three examples: SurgGaze, which captures surgeons’ attention intraoperatively; Surgment, which creates perceptual and cognitive exercises from surgical recordings; and eXplainMR, an ultrasound practice platform that provides explanations for action guidance. Together, these examples show how human-AI systems can make surgical expertise more transferable, accessible, and scalable for medical students.