Academic Journal
Application of Digital Twin Technology to Enhance Chronic Diseases Management: A Systematic Review.
| Title: | Application of Digital Twin Technology to Enhance Chronic Diseases Management: A Systematic Review. |
|---|---|
| Authors: | Zarei, Amirhossein, Gholamzadeh, Marsa, Asadi, Fatemeh, Hu, Fei |
| Source: | International Journal of Telemedicine & Applications; 6/10/2026, Vol. 2026, p1-27, 27p |
| Subject Terms: | Digital twin, Chronic diseases, Medical innovations, Data analytics, Internet of things, Artificial intelligence, Individualized medicine, Machine learning |
| Abstract: | Background: Given that chronic diseases account for a considerable proportion of preventable deaths globally, the adoption of innovative technologies for disease management and prevention is crucial. Digital twins (DTs), representing one of the most advanced technological solutions, enable real‐time simulation and monitoring of chronic disease progression, facilitating personalized treatment strategies and early intervention. This systematic review examines current research on DT applications in chronic disease management to evaluate their potential impact. Methods: A systematic search was conducted in four databases including PubMed, Scopus, Web of Science, and IEEE from inception to the date of the last search. The research question was formulated using PICO framework. Next, all articles were screened following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines to select eligible articles based on inclusion criteria. The extracted information was analyzed to determine the main applications, domains, and employed technologies using quantitative and qualitative techniques. Results: Out of 298 citations, 20 studies met our inclusion criteria after duplicate removal and screening. Most studies (45%, n = 10) were published between 2023 and 2024, indicating an increasing trend in this area. Geographically, the United States contributed the most studies (25%, n = 5), followed by Switzerland (15%, n = 3). Our analysis revealed that primary applications of DT in chronic disease management included medical training and education (65%, n = 13), personalized medicine and patient care (45%, n = 9), and drug discovery and clinical trials (35%, n = 7). Target groups comprised clinicians (42.11%), patients (31.58%), and medical students (15.79%). Key enabling technologies in this subject were data analytics (65%), artificial intelligence and machine learning (60%), computational physiological modeling (30%), and IoT sensors (25%). Conclusions: Our findings demonstrate that DT technology has evolved from theoretical models to integrated clinical applications, with the potential to revolutionize healthcare through personalized medicine, continuous monitoring, and AI‐driven decision support. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Telemedicine & Applications is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Biomedical Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Application of Digital Twin Technology to Enhance Chronic Diseases Management: A Systematic Review. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zarei%2C+Amirhossein%22">Zarei, Amirhossein</searchLink><br /><searchLink fieldCode="AR" term="%22Gholamzadeh%2C+Marsa%22">Gholamzadeh, Marsa</searchLink><br /><searchLink fieldCode="AR" term="%22Asadi%2C+Fatemeh%22">Asadi, Fatemeh</searchLink><br /><searchLink fieldCode="AR" term="%22Hu%2C+Fei%22">Hu, Fei</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Telemedicine & Applications; 6/10/2026, Vol. 2026, p1-27, 27p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Digital+twin%22">Digital twin</searchLink><br /><searchLink fieldCode="DE" term="%22Chronic+diseases%22">Chronic diseases</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+innovations%22">Medical innovations</searchLink><br /><searchLink fieldCode="DE" term="%22Data+analytics%22">Data analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Individualized+medicine%22">Individualized medicine</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Given that chronic diseases account for a considerable proportion of preventable deaths globally, the adoption of innovative technologies for disease management and prevention is crucial. Digital twins (DTs), representing one of the most advanced technological solutions, enable real‐time simulation and monitoring of chronic disease progression, facilitating personalized treatment strategies and early intervention. This systematic review examines current research on DT applications in chronic disease management to evaluate their potential impact. Methods: A systematic search was conducted in four databases including PubMed, Scopus, Web of Science, and IEEE from inception to the date of the last search. The research question was formulated using PICO framework. Next, all articles were screened following Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) guidelines to select eligible articles based on inclusion criteria. The extracted information was analyzed to determine the main applications, domains, and employed technologies using quantitative and qualitative techniques. Results: Out of 298 citations, 20 studies met our inclusion criteria after duplicate removal and screening. Most studies (45%, n = 10) were published between 2023 and 2024, indicating an increasing trend in this area. Geographically, the United States contributed the most studies (25%, n = 5), followed by Switzerland (15%, n = 3). Our analysis revealed that primary applications of DT in chronic disease management included medical training and education (65%, n = 13), personalized medicine and patient care (45%, n = 9), and drug discovery and clinical trials (35%, n = 7). Target groups comprised clinicians (42.11%), patients (31.58%), and medical students (15.79%). Key enabling technologies in this subject were data analytics (65%), artificial intelligence and machine learning (60%), computational physiological modeling (30%), and IoT sensors (25%). Conclusions: Our findings demonstrate that DT technology has evolved from theoretical models to integrated clinical applications, with the potential to revolutionize healthcare through personalized medicine, continuous monitoring, and AI‐driven decision support. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Telemedicine & Applications is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/ijta/2299762 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 27 StartPage: 1 Subjects: – SubjectFull: Digital twin Type: general – SubjectFull: Chronic diseases Type: general – SubjectFull: Medical innovations Type: general – SubjectFull: Data analytics Type: general – SubjectFull: Internet of things Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Individualized medicine Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Application of Digital Twin Technology to Enhance Chronic Diseases Management: A Systematic Review. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zarei, Amirhossein – PersonEntity: Name: NameFull: Gholamzadeh, Marsa – PersonEntity: Name: NameFull: Asadi, Fatemeh – PersonEntity: Name: NameFull: Hu, Fei IsPartOfRelationships: – BibEntity: Dates: – D: 10 M: 06 Text: 6/10/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16876415 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: International Journal of Telemedicine & Applications Type: main |
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