Digital Twin Intelligence in Healthcare: Foundations, Agents, and Strategy

1 Unit · Graduate

In this course, you will be introduced to the design and adoption of AI-enabled digital twinning, the aim of which is to curate dynamic, continuously learning service-oriented solutions that mirror a person’s physiology, behavior, and contextual care. Students will integrate multimodal sensing, machine-learning, and Agentic AI to (1) model functional states such as activity, emotion, and circadian rhythm; (2) generate holistic, outcomes-based care plans; (3) develop prescriptive “clinical sandboxes” that predict risk and compare treatment pathways (i.e., sepsis, mortality, and outcomes) inclusive of social determinants of health; and (4) address data-governance challenges of consent, provenance, and portability. By the end of the course, learners build a functioning Digital Twin Agentic AI Agent service that uses a blend of language and/or vision models based on a digital-twin dataset of their choice.