Advancing Health Care With Digital Twins: Meta-Review of Applications and Implementation Challenges (Preprint) DOI
Mickaël Ringeval, Faustin Armel Etindele Sosso, Martin Cousineau

et al.

Published: Dec. 2, 2024

BACKGROUND Digital twins (DTs) are digital representations of real-world systems, enabling advanced simulations, predictive modeling, and real-time optimization in various fields, including health care. Despite growing interest, the integration DTs care faces challenges such as fragmented applications, ethical concerns, barriers to adoption. OBJECTIVE This study systematically reviews existing literature on DT applications with three objectives: (1) map primary (2) identify key limitations, (3) highlight gaps that can guide future research. METHODS A meta-review was conducted a systematic fashion, adhering PRISMA-ScR (Preferred Reporting Items for Systematic Reviews Meta-Analyses extension Scoping Reviews) guidelines, included 25 published between 2021 2024. The search encompassed 5 databases: PubMed, CINAHL, Web Science, Embase, PsycINFO. Thematic synthesis used categorize stakeholders, RESULTS total 3 were identified: personalized medicine, operational efficiency, medical While current diagnostics, patient-specific treatment hospital resource optimization, remain their early stages development, they significant potential DTs. Challenges include data quality, issues, socioeconomic barriers. review also identified scalability, interoperability, clinical validation. CONCLUSIONS hold transformative care, providing individualized accelerated However, adoption is hindered by technical, ethical, financial Addressing these issues requires interdisciplinary collaboration, standardized protocols, inclusive implementation strategies ensure equitable access meaningful impact. CLINICALTRIAL

Language: Английский

Advancing Healthcare with Digital Twins: A Meta-Review of Applications and Implementation Challenges (Preprint) DOI Creative Commons
Mickaël Ringeval, Faustin Armel Etindele Sosso, Martin Cousineau

et al.

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e69544 - e69544

Published: Jan. 24, 2025

Background Digital twins (DTs) are digital representations of real-world systems, enabling advanced simulations, predictive modeling, and real-time optimization in various fields, including health care. Despite growing interest, the integration DTs care faces challenges such as fragmented applications, ethical concerns, barriers to adoption. Objective This study systematically reviews existing literature on DT applications with three objectives: (1) map primary (2) identify key limitations, (3) highlight gaps that can guide future research. Methods A meta-review was conducted a systematic fashion, adhering PRISMA-ScR (Preferred Reporting Items for Systematic Reviews Meta-Analyses extension Scoping Reviews) guidelines, included 25 published between 2021 2024. The search encompassed 5 databases: PubMed, CINAHL, Web Science, Embase, PsycINFO. Thematic synthesis used categorize stakeholders, Results total 3 were identified: personalized medicine, operational efficiency, medical While current diagnostics, patient-specific treatment hospital resource optimization, remain their early stages development, they significant potential DTs. Challenges include data quality, issues, socioeconomic barriers. review also identified scalability, interoperability, clinical validation. Conclusions hold transformative care, providing individualized accelerated However, adoption is hindered by technical, ethical, financial Addressing these issues requires interdisciplinary collaboration, standardized protocols, inclusive implementation strategies ensure equitable access meaningful impact.

Language: Английский

Citations

3
Overcoming Low Adherence to Chronic Medications by Improving their Effectiveness Using a Personalized Second-generation Digital System DOI
Areej Bayatra,

Rima Nasserat,

Yaron Ilan

et al.

Current Pharmaceutical Biotechnology, Journal Year: 2024, Volume and Issue: 25(16), P. 2078 - 2088

Published: Jan. 29, 2024

Low adherence to chronic treatment regimens is a significant barrier improving clinical outcomes in patients with diseases. result of multiple factors.

Language: Английский

Citations

8
AI-Driven Physical Rehabilitation Strategies in Post-Cancer Care DOI

Faisal Al-Akayleh,

Mayyas Al‐Remawi, Ahmed S.A. Ali Agha

et al.

Published: Feb. 26, 2024

Artificial intelligence (AI) has made significant progress in addressing the specific obstacles related to post-cancer physical rehabilitation. This article examines AI technologies such as Support Vector Machines (SVM), Bayesian Inference, Reinforcement Learning, and Partially Observable Markov Decision Processes (POMDPs), focusing on their potential improve effectiveness adaptability of rehabilitation strategies. SVM is recognized for its capability analyze high-dimensional data obtained from wearable sensors, thereby enabling realtime patient monitoring. methods facilitate flexible adjustment treatment plans, enhancing efficient allocation resources healthcare environments. Learning enables realtime, dynamic optimization robotic-assisted physiotherapy, yet it also raises ethical concerns regarding automated decision-making. POMDPs provide a mathematical framework effectively uncertainties involved care. have personalized adaptive treatments. However, important address considerations privacy, informed consent, algorithmic fairness through further investigation. emphasizes importance interdisciplinary research governance maximizing transforming

Language: Английский

Citations

8
The Constrained Disorder Principle May Account for Consciousness DOI Creative Commons

Tal Sigawi,

Omer Hamtzany, Josef Daniel Shakargy

et al.

Brain Sciences, Journal Year: 2024, Volume and Issue: 14(3), P. 209 - 209

Published: Feb. 23, 2024

There is still controversy surrounding the definition and mechanisms of consciousness. The constrained disorder principle (CDP) defines complex systems by their dynamic borders, limiting inherent disorder. In line with CDP, brain exhibits a bounded borders essential for proper function, efficient energy use, life support under continuous perturbations. brain’s variability contributes to its adaptability flexibility. Neuronal signal challenges association structures consciousness methods assessing present paper discusses some theories about consciousness, emphasizing failure explain variability. This describes how CDP accounts consciousness’s variability, complexity, entropy, uncertainty. Using newly developed second-generation artificial intelligence systems, we describe CDP-based platforms may improve disorders (DoC) accounting platform could be used response current interventions develop new therapeutic regimens patients DoC in future studies.

Language: Английский

Citations

6
Harnessing Variability Signatures and Biological Noise May Enhance Immunotherapies’ Efficacy and Act as Novel Biomarkers for Diagnosing and Monitoring Immune-Associated Disorders DOI Creative Commons

Tal Sigawi,

A Israeli, Yaron Ilan

et al.

ImmunoTargets and Therapy, Journal Year: 2024, Volume and Issue: Volume 13, P. 525 - 539

Published: Oct. 1, 2024

Lack of response to immunotherapies poses a significant challenge in treating immune-mediated disorders and cancers. While the mechanisms associated with poor responsiveness are not well defined change between among subjects, current methods for overcoming loss insufficient. The Constrained Disorder Principle (CDP) explains biological systems based on their inherent variability, bounded by dynamic boundaries that internal external perturbations. Inter intra-subject variability characterize immune system, making it difficult provide single therapeutic regimen all patients even same over time. dynamicity is also personalizing immunotherapies. CDP-based second-generation artificial intelligence system an outcome-based platform incorporates personalized signatures into may improving treatments. offer method identifying new biomarkers early diagnosis, monitoring immune-related disorders, evaluating

Language: Английский

Citations

5
Inter-organ correlations in inflammation regulation: a novel biological paradigm in a murine model DOI Open Access

Yehudit Shabat,

Devorah Rotnemer-Golinkin,

Lidya Zolotarov

et al.

Journal of Medicine and Life, Journal Year: 2025, Volume and Issue: 18(1), P. 67 - 72

Published: Jan. 1, 2025

Interactions between immune system constituents are mediated through direct contact or the transfer of mediators. The study aimed to assess correlation components and out-of-body signals in a model liver inflammation. In first experiment, mice injected with Concanavalin A (ConA) were housed cage tube on top containing healthy livers harvested from ConA. second that contained splenocytes naïve donors treated vitro dexamethasone. Mice tested for serum aspartate aminotransferase (AST) alanine (ALT) levels. External whole spleens influenced immune-mediated inflammatory response mice. When ConA-injected cages tubes mice, ALT levels significantly reduced. elevated when kept part ConA had increased Similarly, dexamethasone-treated also showed data suggest correlations can be established using without

Language: Английский

Citations

0
The constrained disorder principle and the law of increasing functional information: The elephant versus the Moeritherium DOI Creative Commons
Yaron Ilan

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown, P. 100040 - 100040

Published: April 1, 2025

Language: Английский

Citations

0
Using the Constrained Disorder Principle to Navigate Uncertainties in Biology and Medicine: Refining Fuzzy Algorithms DOI Creative Commons
Yaron Ilan

Biology, Journal Year: 2024, Volume and Issue: 13(10), P. 830 - 830

Published: Oct. 16, 2024

Uncertainty in biology refers to situations which information is imperfect or unknown. Variability, on the other hand, measured by frequency distribution of observed data. Biological variability adds uncertainty. The Constrained Disorder Principle (CDP) defines all systems universe their inherent variability. According CDP, exhibit a degree necessary for proper function, allowing them adapt changes environments. Per while differs from uncertainty, it can be viewed as regulated mechanism efficient functionality rather than This paper explores various aspects un-certainties biology. It focuses using CDP-based platforms refining fuzzy algorithms address some challenges associated with biological and medical uncertainties. Developing decision tree that considers natural help minimize method reveal previously unidentified classes, reduce number unknowns, improve accuracy modeling results, generate algorithm outputs are more biologically clinically relevant.

Language: Английский

Citations

3
The constrained-disorder principle defines the functions of systems in nature DOI Creative Commons
Yaron Ilan

Frontiers in Network Physiology, Journal Year: 2024, Volume and Issue: 4

Published: Dec. 18, 2024

The Constrained Disorder Principle (CDP) defines all systems in nature by their degree of inherent variability. Per the CDP, intrinsic variability is mandatory for proper function and dynamically changed based on pressures. CDP boundaries as a mechanism continuous adaptation to internal external perturbations, enabling survival under dynamic conditions. laws govern world's natural phenomena underlie systems. Nevertheless, physics do not entirely explain systems' functionality pressure, which essential determining correct operation complex nature. Variability noise are two broad sources unpredictability biology technology. This paper explores how provides examples from various areas where applies, including climate, genetic, biology, human behavioral variabilities. According system malfunction results inappropriate performance environment influences physiological variability, species interactions influence eco-evolutionary outcomes. behavior being driven randomness accounts malfunctions corrections. reviews variability-based algorithms CDP-based second-generation artificial intelligence potential improving prediction efficiency using

Language: Английский

Citations

3
Digital Twins in Healthcare DOI

Christina Joseph Jyothula,

C. Kishor Kumar Reddy, Thandiwe Sithole

et al.

Advances in medical technologies and clinical practice book series, Journal Year: 2024, Volume and Issue: unknown, P. 48 - 68

Published: June 28, 2024

Digital twin is the virtual representation of a physical system that processes information from its counterpart's environment, used to predict, simulate, and validate system's future behaviour. system, being an emergent technology, has seen implementations in wide array industries such as smart cities, engineering, etc. In healthcare, digital technology shows great promise improve various areas patient care, virtualization hospital spaces, There are concerns regarding data confidentiality, safety, accuracy reliability, avoidance bias, These can be combated only through thorough feedback experts, i.e., healthcare professionals. This chapter aims provide valuable insights into different needs professionals while implementing systems aiding diagnosis, treatment planning, monitoring, collaboration among specialty teams all dealing with security sampling bias.

Language: Английский

Citations

2