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Volume 13
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Review

A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare

by
Silvia L. Chaparro-Cárdenas
1,*,
Julian-Andres Ramirez-Bautista
2,
Juan Terven
3,
Diana-Margarita Córdova-Esparza
4,
Julio-Alejandro Romero-Gonzalez
4,
Alfonso Ramírez-Pedraza
3,5 and
Edgar A. Chavez-Urbiola
3
1
Departamento de Investigación, Universidad de Investigación y Desarrollo-UDI, Cra. 9 No. 10-40, San Gil 684031, Santander, Colombia
2
Departamento de Investigación, Fundación Universitaria de San Gil—Unisangil, Km2 vía San Gil—Charalá, San Gil 684031, Santander, Colombia
3
CICATA-Qro, Instituto Politecnico Nacional, Querétaro 76090, Mexico
4
Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, Mexico
5
Secretaría de Ciencia, Humanidades, Tecnología e Innovación SECIHTI, IxM, Alvaro Obregón, Mexico City 03940, Mexico
*
Author to whom correspondence should be addressed.
Healthcare 2025, 13(14), 1763; https://doi.org/10.3390/healthcare13141763
Submission received: 30 May 2025 / Revised: 8 July 2025 / Accepted: 9 July 2025 / Published: 21 July 2025
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Abstract

Digital transformation is reshaping the healthcare field by streamlining diagnostic workflows and improving disease management. Within this transformation, Digital Twins (DTs), which are virtual representations of physical systems continuously updated by real-world data, stand out for their ability to capture the complexity of human physiology and behavior. When coupled with Artificial Intelligence (AI), DTs enable data-driven experimentation, precise diagnostic support, and predictive modeling without posing direct risks to patients. However, their integration into healthcare requires careful consideration of ethical, regulatory, and safety constraints in light of the sensitivity and nonlinear nature of human data. In this review, we examine recent progress in DTs over the past seven years and explore broader trends in AI-augmented DTs, focusing particularly on movement rehabilitation. Our goal is to provide a comprehensive understanding of how DTs bolstered by AI can transform healthcare delivery, medical research, and personalized care. We discuss implementation challenges such as data privacy, clinical validation, and scalability along with opportunities for more efficient, safe, and patient-centered healthcare systems. By addressing these issues, this review highlights key insights and directions for future research to guide the proactive and ethical adoption of DTs in healthcare.
Keywords: big data analysis; digital health; digital twins; health innovation; real-time communication big data analysis; digital health; digital twins; health innovation; real-time communication

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MDPI and ACS Style

Chaparro-Cárdenas, S.L.; Ramirez-Bautista, J.-A.; Terven, J.; Córdova-Esparza, D.-M.; Romero-Gonzalez, J.-A.; Ramírez-Pedraza, A.; Chavez-Urbiola, E.A. A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare. Healthcare 2025, 13, 1763. https://doi.org/10.3390/healthcare13141763

AMA Style

Chaparro-Cárdenas SL, Ramirez-Bautista J-A, Terven J, Córdova-Esparza D-M, Romero-Gonzalez J-A, Ramírez-Pedraza A, Chavez-Urbiola EA. A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare. Healthcare. 2025; 13(14):1763. https://doi.org/10.3390/healthcare13141763

Chicago/Turabian Style

Chaparro-Cárdenas, Silvia L., Julian-Andres Ramirez-Bautista, Juan Terven, Diana-Margarita Córdova-Esparza, Julio-Alejandro Romero-Gonzalez, Alfonso Ramírez-Pedraza, and Edgar A. Chavez-Urbiola. 2025. "A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare" Healthcare 13, no. 14: 1763. https://doi.org/10.3390/healthcare13141763

APA Style

Chaparro-Cárdenas, S. L., Ramirez-Bautista, J.-A., Terven, J., Córdova-Esparza, D.-M., Romero-Gonzalez, J.-A., Ramírez-Pedraza, A., & Chavez-Urbiola, E. A. (2025). A Technological Review of Digital Twins and Artificial Intelligence for Personalized and Predictive Healthcare. Healthcare, 13(14), 1763. https://doi.org/10.3390/healthcare13141763

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