ReviewSensors (Basel, Switzerland)2023
A Comprehensive Review of Digital Twin from the Perspective of Total Process: Data, Models, Networks and Applications.
Review in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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
9 citing papers in PubMed.
- A MOOC-based and high-fidelity surgical simulation-integrated clinical internship mode in optometry teaching for ophthalmology graduate students.BMC medical education · 2026Trial
- A digital twin and deep-learning ensemble for cyber attack detection in industrial control systems at the IoT edge.Scientific reports · 2026Article
- Development of the Windmill Model for Mapping Older Adults' Intrinsic Capacity Using Digital Twin Technology: Descriptive Qualitative Study.JMIR aging · 2026Article
- AKI-twinX: explainable organ structured digital twin for sepsis AKI trajectory forecasting.medRxiv : the preprint server for health sciences · 2026Article
- An Integrated Cyber-Physical Digital Twin Architecture with Quantitative Feedback Theory Robust Control for NIS2-Aligned Industrial Robotics.Sensors (Basel, Switzerland) · 2026Article
- Transformative roles of digital twins from drug discovery to continuous manufacturing: pharmaceutical and biopharmaceutical perspectives.International journal of pharmaceutics: X · 2025Review
- Development of an Advanced Multi-Layer Digital Twin Conceptual Framework for Underground Mining.Sensors (Basel, Switzerland) · 2025Review
- Microwave Digital Twin Prototype for Shoulder Injury Detection.Sensors (Basel, Switzerland) · 2024Article
- Tools, Technologies and Frameworks for Digital Twins in the Oil and Gas Industry: An In-Depth Analysis.Sensors (Basel, Switzerland) · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
With the rapid development of industrial digitalization and intelligence, there is an urgent need to accurately depict the physical world in digital space, and, in turn, regulate and optimize the behavior of physical entities based on massive data collection and analysis. As a technology that combines virtual space and physical space, digital twin can satisfy all of the above needs, and has attracted widespread attention. Due to the promising application prospects of digital twins, both academia and industry have launched research in this field, and related studies have been conducted from different perspectives. Accordingly, some articles summarizing the existing work have also been published, but they are all from a single perspective, lacking a systematic introduction and summary. Based on this, this paper conducts a comprehensive review of the existing work on digital twins from four perspectives: data, model, network and application, and strives to gain a better understanding of the development of the field from the physical to the virtual and back to the physical. Meanwhile, current research challenges and future directions for the development of digital twins are all discussed.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.