Towards building AI Assistants for Doctors: Vision, Language, and Interaction
Medical imaging, including magnetic resonance imaging (MRI) and computed tomography (CT), is central to modern diagnosis and treatment planning. Although contemporary radiological AI systems can achieve fast and accurate diagnoses, most offer limited user interaction within clinical workflows. This gap hinders adoption by reducing transparency, adaptability, and trust. This talk will showcase research from our Health-X Lab on building radiological AI systems designed to collaborate with clinicians through intuitive and natural interaction channels. Specifically, I will highlight three lines of work: leveraging human visual attention during radiological reading to better align AI models with expert behavior; enabling flexible, language-based interaction with medical images; and developing interactive AI agents that support real-time, user-driven analysis. Together, these approaches illustrate how integrating visual perception, language, and interaction can transform AI from passive tools into effective clinical co-pilots, enhancing usability, interpretability, and seamless workflow integration.
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- 3775 Rue University
- Montreal, Quebec
- Canada
- Building: Duff Medical Building
- Room Number: 321
- Starts 07 May 2026 05:00 AM UTC
- Ends 18 June 2026 03:00 PM UTC
- No Admission Charge
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Towards building AI Assistants for Doctors: Vision, Language, and Interaction
Biography:
Dr. Yiming Xiao is an Associated Professor in Computer Science and Software Engineering, a Concordia University Research Chair in Intuitive and Intelligent Surgical Technologies, member of the Applied AI Institute and the School of Health at Concordia, and a FRQS Junior 1 Research Scholar. He obtained his Ph.D. in Biomedical Engineering at McGill University in 2016. Soon after, he joined the PERFORM Centre as a PERFORM Postdoctoral Fellow. From 2018-2020, he was a CIHR and BrainsCAN Postdoctoral Fellow at the Robarts Research Institute of Western University. Yiming’s research combines novel techniques in medical imaging principles, computer vision, mixed reality, and deep learning to improve the efficiency and accuracy of image-based diagnosis and image-guided surgery.
