The digital twin as a virtual companion of a physical system has had many applications in the fields of aerospace engineering, manufacturing, construction, urban planning, and automotive, significantly improving efficiency and lowering cost. Recently, the concept of digital twins for health has emerged as a hot topic in the artificial intelligence (AI) and healthcare community. It brings promise to provide an AI-enabled, individualized healthcare solution to transform healthcare at both the caregivers’ and patients’ ends. The development and deployment of digital twins in healthcare settings require multiple disciplinary collaborations and concerted efforts from all healthcare stakeholders, including AI researchers, industrial partners, policymakers, clinicians and patients. This workshop aims to explore this timely topic with relevant domain experts and practitioners to have an in-depth discussion on how to develop AI-enabled digital twins to improve the quality of healthcare while lowering its spiraling cost.
The workshop will be open for the whole conference. Each submitted paper will be evaluated by three reviewers from the aspects of novelty, significance, technique sound, experiments, and presentations. The reviewers will be program committee members or researchers recommended by the members.
The selected workshop papers will be extended and published by Journal of Data Intelligence.
All papers submitted should have a maximum length of 8 pages and demo papers should be no more than 4 pages. All must be prepared using the ACM camera-ready template. Authors are required to submit their papers electronically in PDF format.
Multimodal data acquisition
Data standards and annotation for AI/ML
Real-world data integration and curation
Mathematical, statistical, and mechanistic modeling of organs and systems
Modeling and simulations of time-series data
Tumor microenvironment modeling and simulations
Physics-informed machine learning
Ethical and trustworthy AI for healthcare
Responsible and explainable AI for healthcare
Security and privacy in clinical AI
Robust and interpretable natural language processing for healthcare
AI and bioinformatics for improved healthcare
Mobile health for monitoring and intervention
AI-assisted clinical decision support
Knowledge representation and extraction