Digital twins for precision health

A digital twin for precision health is a set of virtual information constructs that mimics the structure, context, and behavior of a human body or health systems (or system-of-systems), is dynamically and continuously updated with data from its physical twin, has a predictive capability, its correctness can be verified and informs decisions that realize value and resemble actionable information to guide health and wellness care delivery. The bidirectional interaction between the virtual and the physical is central to the digital twin. The bidirectional flow of information from the human health system to the computational model affects a twin that remains tightly coupled with the human health system leading to more effective identification of risk factors in the presence of current or future behavior, and/or adverse events. 

[definition adopted from 1. National Academies of Sciences, Engineering, and Medicine. 2023. Foundational Research Gaps and Future Directions for Digital Twins. Washington, DC: The National Academies Press. and 2. AIAA (American Institute of Aeronautics and Astronautics). Digital Engineering Integration Committee. 2020. “Digital Twin: Definition & Value.” AIAA and AIA Position Paper, AIAA, Reston, VA.)

Potential topics could include: 
 

1. Foundations of digital twins for precision health
2. Novel sensors and models for digital twins
3. Artificial intelligence (AI) and digital twins
4. Digital twins for health, including but not limited to: 

• Cardiovascular Health
• Mental and Behavioral Health
• Neurological Disorders and Stroke Prevention
• Infectious Diseases
• Diabetes

This Collection supports and amplifies research related to SDG 3: Good health and wellbeing.