ArticleAnnual reviews in control2025
Cyber-Physical-Human Systems in Precision Medicine: Advances in Artificial Pancreas for Treatment of Diabetes.
Article in Annual reviews in control, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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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.
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Who cites it
2 citing papers in PubMed.
- Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management.Frontiers in endocrinology · 2026Review
- Development and validation of an AI-driven blood glucose prediction system for pilots based on continuous glucose monitoring.Frontiers in physiology · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
Cyber-physical-human systems (CPHS) hold the potential to transform healthcare delivery and patient outcomes for numerous chronic diseases, such as diabetes, through remote patient monitoring, automatic control, and precision medicine. Expedited by the recent advances in artificial intelligence, including model development, control systems, data assimilation, network infrastructure, and cybersecurity, the application of digital twins has proliferated across various industry sectors, and have recently been applied to medical settings. Digital twins of people with type 1 diabetes (T1D) and the pancreas can well represent the complex metabolic, physiologic, and pharmacologic processes underlying the chronic disease. This enables intelligent CPHSs that can automate insulin delivery without any manual user announcements to mitigate the effects of various disturbances to glucose homeostasis such as meals, physical activities, acute psychological stress, and sleep pattern variations. Automated insulin delivery in people with T1D, also called artificial pancreas, is a successful application of digital twins in medicine that advances T1D treatment, reducing the burden of the chronic condition and improving the lives of people with T1D. We present a hybrid modeling framework that integrates mechanistic physiological models with data-driven empirical models to develop accurate digital twins of people with T1D, which is then used in an artificial intelligence-enabled automated insulin delivery system. Simulation and clinical experiments integrating virtual patients or individuals with T1D with an artificial pancreas system illustrate the performance of the CPHS, demonstrating the capabilities of rendering fully-automated real-time treatment decisions for precision medicine. Future avenues of research and development in CPHS for precision medicine are also highlighted, including online learning algorithms, adaptive fault-tolerant systems, and robust cybersecurity.
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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.