in Conjunction with the 14th IEEE International Conference on Healthcare Informatics ICHI 2026 June 1, 2026, Minneapolis, MN, USA

 Overview

Health digital twins, although no consensus on precise definitions, are dynamic, computational representations of health and disease that span multiple levels of biological and social organization, from cells and organs to individuals, families, communities and populations. Enabled by advances in multimodal data integration (of clinical, physiological, behavioral, imaging, and molecular data), AI foundation models, mechanistic and causal modeling, and interoperable digital infrastructures, health digital twins hold the potential to transform prediction, prevention, monitoring, and tailored clinical decision-making across the continuum of care and human health.

This workshop convenes researchers, clinicians, data and standards experts, ethicists, and regulators, and industry leaders to explore the emerging science and practice and the technical, clinical, and socio‑technical challenges of building trustworthy, multi-scale health digital twins. Through invited talks, demonstrations, and focused interactive working group discussions, participants will examine state-of-the-art approaches to data harmonization, multi-scale and hybrid modeling, validation and evaluation, and governance frameworks that embed transparency, equity, and human oversight. The goal is to align technical innovation, establish best practices, and develop a research agenda with clinical development and patient needs, accelerating responsible translation from research to real-world impact.

 Call for Papers

Health digital twins, dynamic, computational representations of health that evolve over time and span individuals, communities, and populations that integrate multimodal data and muti-scale models, are an emerging paradigm with the potential to transform clinical development and how we diagnosis, monitor, and personalize care in the real-world setting. This workshop invites submissions exploring the design, evaluation, deployment, and governance of health digital twins with a particular emphasis on data interoperability, model validity, explainability, real-world clinical and population integration, and governance frameworks that enable trustworthy, equitable, responsible use, and clinical development impact in human‑centered health care.

We welcome work spanning methods, applied systems, clinical pilots, datasets, standards, ethics, and socio‑technical perspectives.

Scope and topics of interest

• Data architectures, standards, and interoperability (EHR, FHIR, OMOP, open EHR, multimodal fusion)
• Multi‑scale and hybrid modeling (mechanistic + data‑driven integration)
• Real‑time sensing, streaming updates, and digital biomarkers from wearables
• Clinical decision support and closed‑loop systems informed by patient twins
• Validation, benchmarking, and clinical evaluation protocols for twins
• Explainability, visualization, and clinician/patient interaction design for trust
• Privacy‑preserving methods, federated learning and data governance in twins
• Ethical, legal, and regulatory considerations for deployment and liability
• Datasets, synthetic data, and reproducibility practices for twin research
• Translational case studies: Oncology, Immunology, Cardiovascular disease, Neuroscience

 Submission & Review

We are inviting contributions to the Digital Patient Twins, including regular papers, short papers, and abstract for posters.

• Regular papers (8-10 pages, including references) will describe mature ideas, where a substantial amount of implementation, experimentation, or data collection and analysis has been completed.
• Short papers (4-6 pages, including references) will describe innovative ideas, where preliminary implementation and validation work have been conducted.
• Abstracts (1 page, including references) will describe your vision, work in progress and preliminary results.

 Organizing Committee

Organizer

• Stephen Huo, PhD, MD, JHuo5@its.jnj.com, Senior Director of R&D Integrated Evidence and Advanced Patient Modeling, Johnson & Johnson Innovative Medicine
• Jiang Bian, PhD, FACMI, bianji@iu.edu, Associate Dean of Data Science, Walther and Regenstrief Professor of Cancer Informatics, Professor of Biostatistics & Health Data Science, Chief Data Scientist for Regenstrief Institute, and Chief Data Scientist for Indiana University Health.
• Yu Huang, PhD, yh60@iu.edu, Assistant Professor in the Department of Biostatistics and Health Data Science at the Indiana University School of Medicine.
• Huanmei Wu, PhD, huanmei.wu@temple.edu, Professor and Department Chair of Health Services Administration and Policy; Assistant Dean for Global Engagement, Temple University.
• Yi Qian, PhD, Yqian10@its.jnj.com, Vice President, R&D Integrated Evidence and Advanced Patient Modeling, Johnson & Johnson Innovative Medicine.

 Important Dates

Your submissions must adhere to the IEEE Proceedings Format. All papers will be submitted and handled through EasyChair. Submissions must contain the names and affiliations of authors listed on the paper. The review process will be double-blinded.

• Deadline for abstract/paper submission: March 15, 2026
• Notification of workshop paper acceptance: March 21, 2026
• Camera ready workshop paper due: March 28, 2026
• Workshop date: June 1, 2026

 Schedule

Sample agenda (3.5 hours) with 12 presenters

• 0:00–0:10 — Welcome + Opening keynote
• 0:15–1:00 — Technical session I: 3 short talks (12 min talk + 3 min Q each)
• 1:00–1:45 — Technical session II: 3 short talks (12 min talk + 3 min Q each)
• 1:45–2:00 — Coffee break + demo view (light refreshments)
• 2:00–2:40 — Clinical cases I: 3 short talks (10 min talk + 3 min Q each)
• 2:40–3:20 — Clinical cases II: 3 short talks (10 min talk + 3 min Q each)
• 3:20–3:30 — Closing summary

 Registration

For more information about IEEE ICHI 2026, please visit https://zhang-informatics.github.io/ICHI2026/