ReviewNPJ systems biology and applications2024
Immune digital twins for complex human pathologies: applications, limitations, and challenges.
Review in NPJ systems biology and applications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Molecular exploration of host-pathogen interactions in severeFrontiers in medicine · 2025Pooled it
- Clinical trials in a dish: cardiometabolic drug development with biological and digital twins.The Journal of clinical investigation · 2026Review
- Nanotechnology in Pediatric Neurology: Applications and Innovations.Pharmaceutics · 2026Review
- Advances and Clinical Translation Potentials of Functional Nanomaterials in Tissue Engineering.Bioengineering (Basel, Switzerland) · 2026Review
- Inflammatory marker-driven deep learning model for postoperative gastric cancer prognosis.BMC medical informatics and decision making · 2026Article
- Article
- Beyond scarring: a next-generation vision for pulmonary fibrosis management.Molecular biology reports · 2026Review
- Digital twins to accelerate target identification and drug development for immune-mediated disorders.FEBS open bio · 2026Review
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Digital twins for plant-microbe interactions: Gap finding and filling.Plant communications · 2026Article
- TumorTwin: a Python framework for patient-specific digital twins in oncology.BMC medical informatics and decision making · 2026Article
- AI and network biology for rational polypharmacology in signaling drug design: a review.NPJ precision oncology · 2026Review
- Artificial Intelligence Virtual Organoids (AIVOs).Bioactive materials · 2026Review
- A unified digital twin framework for predicting therapeutic response to central nervous system infections by pathogenic free-living amoebae.Parasitology research · 2026Review
- Hybrid Systems Modeling of Donor T-Cell Responses and Graft-Versus-Host-Disease After Posttransplantation Cyclophosphamide Administration.CPT: pharmacometrics & systems pharmacology · 2026Article
- The Potential of Digital Twins for Pediatric Rare Diseases.CPT: pharmacometrics & systems pharmacology · 2026Review
- From FAIR to CURE: guidelines for computational models of biological systems.NPJ systems biology and applications · 2026Review
- A Computational Model of Tumor Interactions with Bone-Resident Cells Predicts Tumor-Type-Specific Responses to Perturbations.bioRxiv : the preprint server for biology · 2026Article
- Artificial intelligence in immunotherapy: revolutionizing diagnostic and therapeutic applications in cancer and autoimmune diseases.Clinical and experimental medicine · 2026Review
- A digital twin forJournal of medical microbiology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
47 authors.
Funding
Abstract
Digital twins represent a key technology for precision health. Medical digital twins consist of computational models that represent the health state of individual patients over time, enabling optimal therapeutics and forecasting patient prognosis. Many health conditions involve the immune system, so it is crucial to include its key features when designing medical digital twins. The immune response is complex and varies across diseases and patients, and its modelling requires the collective expertise of the clinical, immunology, and computational modelling communities. This review outlines the initial progress on immune digital twins and the various initiatives to facilitate communication between interdisciplinary communities. We also outline the crucial aspects of an immune digital twin design and the prerequisites for its implementation in the clinic. We propose some initial use cases that could serve as "proof of concept" regarding the utility of immune digital technology, focusing on diseases with a very different immune response across spatial and temporal scales (minutes, days, months, years). Lastly, we discuss the use of digital twins in drug discovery and point out emerging challenges that the scientific community needs to collectively overcome to make immune digital twins a reality.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.