Digital twins for cardiopulmonary medicine: the case for pulmonary arterial hypertension

American Journal of Respiratory and Critical Care Medicine 2026 Open access

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Authors: Steven A. Niederer, Maximilian Balmus, Christopher Burr, Timothy J A Chico, Richard H. Clayton, Wenjia Bai, C. Rangel-Smith, David J Wagg, Richard D Wilkinson, Sheng‐Ya Wang, Alexander M K Rothman, M R Wilkins
DOI: 10.1093/ajrccm/aamag082
Cited by: 1
References: 66

Abstract

The management of pulmonary arterial hypertension (PAH), like so many diseases, currently relies on episodic data from clinic visits, which offers limited insight into a patient's disease trajectory and dynamic clinical decisions. Patient digital twins present a technological solution that combines hospital and community data into a single dynamic and predictive virtual representation of the patient. Digital twins operationalize real-world observational inference at the individual level, functioning as a complementary decision-support tool alongside trial evidence. This review introduces the concept of a patient digital twin and provides a roadmap for the development, evaluation, and potential implementation of a patient digital twin for PAH. The resulting twin has the potential to change PAH care pathways, shifting PAH care from reactive to proactive management.

Fields of Study & Subjects

Pulmonary Hypertension Research and Treatments Mobile Health and mHealth Applications Delphi Technique in Research

Publication details

Journal / source
American Journal of Respiratory and Critical Care Medicine
Publisher
Oxford University Press (OUP)
Volume
212
Issue
6
Pages
1203–1213
Publication date
20 February 2026
Language
EN