Digital Twin Healthcare and Robotics

Aims & Scope

This session aims to explore the transformative role of digital twin technology in advancing healthcare and robotic applications. It will highlight cutting-edge research and practical implementations that enhance medical precision, personalized treatment, and autonomous systems. The session will foster discussions on interdisciplinary approaches integrating AI, robotics, and data analytics to address current challenges and future opportunities in digitalized healthcare.                                                                              

The session will focus on the following points

• AI-driven predictive modeling for digital twins in medical diagnostics and therapy.
• Integration of digital twins with robotic systems for minimally invasive surgery and autonomous operations.
• Innovative applications of digital twins in mental health assessment.

Session Chairs

Presentations

  • Associate Professor
    Massachusetts General Hospital/Harvard Medical School
    Title: Generative Artificial Intelligence in Digital Medicine and Healthcare
    Abstract

    To be confirmed

  • Associate Professor
    Shandong University
    Title: Minimally Invasive Surgical Robot
    Abstract

    To be confirmed

  • Research Professor
    Center for Psychological Sciences, Zhejiang University
    Title: Objective and Automated Psychiatric Assessment with Gaze-Tracking Robotics
    Abstract Traditional psychiatric assessment for conditions like depression, schizophrenia, and mild cognitive impairment (MCI) relies heavily on subjective self-reporting and clinical observation. These methods are susceptible to limitations, including language barriers, cultural biases, and a patient's willingness to articulate their symptoms. Gaze-tracking technology offers a promising alternative by providing objective, non-invasive biomarkers of brain function. The analysis of gaze patterns—how an individual scans a scene or tracks moving objects—can reveal unique, quantifiable signatures associated with specific neuropsychiatric conditions. To translate this potential into clinical practice, we developed an all-in-one eye-tracking device for accessible use. To alleviate clinician workload and enhance patient access, we integrated this technology into a service robot capable of autonomously administering standardized assessments at the bedside. Empirical results demonstrate that our eye-tracking-based method is effective in detecting conditions such as MCI and schizophrenia. Critically, the robotic system significantly reduces the workload of healthcare workers while delivering data of equivalent quality to the desktop platform. These findings demonstrate a practical path toward objective, accessible, and efficient psychiatric assessments in both hospitals and community settings.
  • Lecturer
    Southern University of Science and Technology
    Title: Human Body Enhancement and Rehabilitation Robot
    Abstract Human motion repair and enhancement robots have the potential to fix human motion deficiencies and enhance human movement performance. However, as research progresses, the compatibility issue between robots and human movement has become increasingly prominent, resulting in incoherent human-machine movement, unnatural gait, and inflexible actions. In-depth research on the principles of human dynamic walking and neuro-biomechanics will inspire robot researchers to reconsider the current design principles and control ideas of wearable robots, potentially resolving the current research dilemmas of motion repair and enhancement robots, and thereby enhancing the degree of human-machine integration. This report will introduce several wearable human enhancement and rehabilitation robots designed by us, such as power thigh prostheses, rehabilitation exoskeletons, and centaur robots, which can effectively repair and enhance human movement abilities in daily environments.