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Background: The concept of digital twins, widely adopted in industry, is entering health care. However, there is a lack of consensus on what constitutes the digital twin of a patient., Objective: The objective of this scoping review was to analyze definitions and characteristics of patient digital twins being developed for clinical use, as reported in the scientific literature., Methods: We searched PubMed, Scopus, Embase, IEEE, and Google Scholar for studies claiming digital twin development or evaluation until August 2023. Data on definitions, characteristics, and development phase were extracted. Unsupervised classification of claimed digital twins was performed., Results: We identified 86 papers representing 80 unique claimed digital twins, with 98% (78/80) in preclinical phases. Among the 55 papers defining "digital twin," 76% (42/55) described a digital replica, 42% (23/55) mentioned real-time updates, 24% (13/55) emphasized patient specificity, and 15% (8/55) included 2-way communication. Among claimed digital twins, 60% (48/80) represented specific organs (primarily heart: 15/48, 31%; bones or joints: 10/48, 21%; lung: 6/48, 12%; and arteries: 5/48, 10%); 14% (11/80) embodied biological systems such as the immune system; and 26% (21/80) corresponded to other products (prediction models, etc). The patient data used to develop and run the claimed digital twins encompassed medical imaging examinations (35/80, 44% of publications), clinical notes (15/80, 19% of publications), laboratory test results (13/80, 16% of publications), wearable device data (12/80, 15% of publications), and other modalities (32/80, 40% of publications). Regarding data flow between patients and their virtual counterparts, 16% (13/80) claimed that digital twins involved no flow from patient to digital twin, 73% (58/80) used 1-way flow from patient to digital twin, and 11% (9/80) enabled 2-way data flow between patient and digital twin. Based on these characteristics, unsupervised classification revealed 3 clusters: simulation patient digital twins in 54% (43/80) of publications, monitoring patient digital twins in 28% (22/80) of publications, and research-oriented models unlinked to specific patients in 19% (15/80) of publications. Simulation patient digital twins used computational modeling for personalized predictions and therapy evaluations, mostly for one-time assessments, and monitoring digital twins harnessed aggregated patient data for continuous risk or outcome forecasting and care optimization., Conclusions: We propose defining a patient digital twin as "a viewable digital replica of a patient, organ, or biological system that contains multidimensional, patient-specific information and informs decisions" and to distinguish simulation and monitoring digital twins. These proposed definitions and subtypes offer a framework to guide research into realizing the potential of these personalized, integrative technologies to advance clinical care., (©David Drummond, Apolline Gonsard. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 13.11.2024.)

It is a challenge to keep abreast of all the clinical and scientific advances in the field of respiratory medicine. This article contains an overview of laboratory-based science, clinical trials and qualitative research that were presented during the 2023 European Respiratory Society International Congress within the sessions from the five groups of Assembly 1 (Respiratory Clinical Care and Physiology). Selected presentations are summarised from a wide range of topics: clinical problems, rehabilitation and chronic care, general practice and primary care, electronic/mobile health (e-health/m-health), clinical respiratory physiology, exercise and functional imaging., Competing Interests: Conflict of interest: C-Y. Hui is a visitor at the University of Edinburgh and is a senior consultant in healthcare sustainability at Turner and Townsend. Her research with the University of Edinburgh is independent from, and not financially supported by, Turner and Townsend. Her views in this publication and the ERS Congress are her own, and not those of Turner and Townsend. Neither she, nor Turner and Townsend, stand to gain financially from work reported in this publication. Conflict of interest: C. Vicente reports personal fees from AstraZeneca, Boehringer Ingelheim, GlaxoSmithKline and Mylan, outside the submitted work. Conflict of interest: V. Poberezhets reports personal fees from AstraZeneca outside the submitted work. Conflict of interest: F.M.E. Franssen reports grants and personal fees from AstraZeneca, Chiesi, GlaxoSmithKline and Sanofi, and personal fees from Pieris, outside the submitted work. Conflict of interest: I. Vogiatzis is an associate editor of this journal. Conflict of interest: T. Gille reports personal fees from Roche SAS, outside the submitted work. Conflict of interest: The other authors have nothing to disclose., (Copyright ©The authors 2024.)