DCMB Tools & Technology Seminar: Jingying Wang
Medical Science Building 1 (MS1), Room 4B700
ZOOMAbout This Event
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.
Presenters
Jingying Wang
PhD Student (Computer Science and Engineering)
My research lies at the intersection of Human-Computer Interaction (HCI) and medical education, with a focus on enhancing the visual understanding in medical training. Medical procedures are inherently visual tasks that require learners to know where to look, how to interpret complex visual cues, and how to translate those perceptions into precise actions. Due to limited access to experts, trainees often learn from videos and simulations that lack personalized and interactive feedback. My work bridges this gap by designing human–AI systems that enable interactive learning experiences grounded in multimodal data sources, including video, gaze, speech, hand gestures, and etc.
Host/Moderator
Marci Brandenburg
Librarian, Library - Health Sciences, University Library
