The purpose of the Digital Twin Health Affiliate Program is to make computational "digital twins" reliable and safe enough for clinical use. While their potential is widely recognized,
fundamental technical barriers around data integration, model validation, and integrated deployment prevent these models from becoming a clinical reality.
We propose to develop a Digital Twin (DT) by integrating baseline multi-modal data and repeated measurements for real-time dynamic model training and updating. We will use innovations in biomedical
research, artificial intelligence, and computing technologies to realize a DT to help decide treatment strategies.
As a first proof of principle, we propose an adaptive dynamic DT to predict response for cancer patients to initial therapy as well as predicting resistance and enable rapid and effective treatment reassignment.
This DT will help physicians make initial treatment determinations, monitor treatment response and effectiveness, and decide when to discontinue or change approach.
This program is subject to Stanford's policies linked here.
To be announced.
Tina Hernandez-Boussard, PhD, Professor of Medicine (Biomedical Informatics), of Biomedical Data Science, of Surgery and, by courtesy, of Epidemiology and Population Health.
Profile.
Olivier Gevaert, PhD, Associate Professor of Medicine (Biomedical Informatics and Biomedical Data Science.
Profile.
Knowledge and Expertise: Collaborate and learn from world-renown digital twin experts, gain from their insight and expertise, and benefit from interacting with their labs' researchers.
Advance Foundational Science: Develop novel methods for hybrid (mechanistic + AI) modeling and multi-modal data fusion to create a new paradigm for in silico medicine.
Train the Next Generation: Support a cohort of interdisciplinary students and postdocs at the intersection of computer science, engineering, and clinical medicine.
De-Risk Clinical Translation: Reduce the cost and time of clinical trials by using digital twins for patient stratification, trial design optimization, and generating high-quality synthetic control arms.
Build an Open Ecosystem: Produce open-source tools, benchmark datasets, and validated prototype twins for ≥3 clinical use cases to establish a new standard for the field.
We offer three tiers of membership, designed to provide varying levels of strategic engagement and resource access. All fees are annual.
Partner Tier | $350,000: Includes, seat on the joint Industry Steering Committee, which guides the program's strategic research roadmap and priority use cases. Sponsorship of a dedicated postdoctoral fellow or two graduate students, with direct collaboration on a project of mutual interest. Priority access to all pre-publication research, IP, the secure evaluation sandbox, and benchmark datasets. Ability to initiate and co-lead focused, milestone-driven "sprint" projects with faculty.
Collaborator Tier | $200,000: Nominate projects for the annual research cycle and participate in project-specific meetings with faculty and trainees. Access to the pool of affiliated students and postdocs for internship recruitment and collaborative projects. Full access to the shared validation infrastructure, open-source tools, and synthetic data upon release. Invitations to an annual exclusive symposium and quarterly technical webinars.
Associate Tier | $50,000: Receive all published research outputs, white papers, and annual progress reports. Invitation to the annual symposium and general community events for networking and trend-spotting. Early visibility into the program's research pipeline and emerging talent for recruitment.