In the rapidly evolving healthcare landscape, there is a growing emphasis on personalized and patient-centered medical practices. This shift recognizes patients as unique individuals rather than mere collections of symptoms. Within this framework, Digital Twin is emerging as a promising tool for creating dynamic, real-time virtual models of patient pathophysiology, allowing healthcare professionals to tailor interventions to each patient's unique needs. However, successfully adopting this technology in clinical settings requires addressing the complexities of implementation, from design to deployment. Many digital health solutions have shown tangible benefits, but the current trend often emphasizes process efficiency over fully leveraging the data semantics within modern systems. This focus can diminish the advantages of a person-centered approach, impacting care quality and patient satisfaction. Digital Twins bridge this gap by prioritizing data, which is essential for simulations and predictive analyses that anticipate a patient’s condition progression. This enables proactive and personalized interventions, enhancing patient compliance and significantly reducing diagnostic tests, referrals, and malpractice complaints. Furthermore, real-time digital replicas of individuals allow patients to actively participate in their care, transforming decision-making, especially in critical medical situations and chronic home care settings. Notably, research underscores the importance of semantic web technologies, such as knowledge graphs and ontologies, in structuring clinical data into machine-readable formats, forming the foundation of effective Digital Twin systems. While Digital Twin technology shows immense promise, barriers to adoption remain. From the legal frameworks surrounding medical device regulations and data privacy laws (e.g., GDPR) to stakeholder engagement and the need for explainable, trustworthy AI embedded within Digital Twins, a comprehensive approach is essential for successful implementation. Furthermore, attention to the interoperability of these technologies with legacy systems and the capacity to automate and personalize care for home-bound patients is crucial for fostering widespread adoption. This Research Topic seeks to explore innovative approaches that unlock the full potential of Digital Twins for accelerating the transition towards modern, person-centric healthcare. By assembling contributions from diverse interdisciplinary perspectives, we aim to harness the collective expertise to propel the evolution of healthcare delivery.This research topic welcomes all article types accepted by Frontiers in Digital Health. Suitable themes for manuscripts include:• Digital Twin engineering for personalized healthcare applications: Focuses on developing and applying Digital Twins tailored for individual patients, integrating real-time physiological, genetic, and environmental data to create accurate virtual models that simulate and predict health outcomes. • Trustworthy embedded AI: Emphasizes integrating transparent, reliable AI within Digital Twins to enhance their predictive and diagnostic capabilities while ensuring data security, algorithmic fairness, and compliance with regulatory standards. • Ontologies and Explainable AI in human-centric healthcare systems: Examines how ontologies and semantic web technologies contribute to the implementation of Digital Twins by enhancing the transparency and interpretability of AI models. This theme explores how structured ontologies can support the integration of Digital Twins into healthcare systems by providing a standardized framework for understanding clinical data, predictions, and decisions, thus improving the reliability and effectiveness of their applications in practice. • Leveraging data-driven technologies to optimize care for home-bound patients: Investigates the role of Digital Twins in conjunction with advanced data analytics, remote monitoring systems, and wearable devices to enhance the care of homebound patients. This theme focuses on how Digital Twin technology can reduce hospital readmissions and improve quality of life by integrating real-time data into actionable insights for home-based care. • Patient engagement through pervasive health informatics solutions: Focuses on integrating Digital Twins into user-friendly health informatics tools like mobile apps and patient portals to enhance patient engagement. This theme explores how Digital Twins can foster patient education, improve communication, and support treatment adherence by providing personalized and actionable health insights. • Case studies and real-world applications demonstrating the impact of person centric healthcare approaches exploiting data-driven technologies: Seeks examples of successful implementations of Digital Twins, AI, and digital health tools, highlighting outcomes, lessons learned, and best practices. • Enhancing Interoperability and Automating Healthcare Delivery through Data Standardization and EHR Implementation: Focuses on converting health data to standardized formats and implementing Electronic Health Records (EHRs) to replace clipboards, reduce provider burden, feed Digital Twins, and trigger autonomous processes, with an emphasis on improving interoperability across healthcare systems.
In the rapidly evolving healthcare landscape, there is a growing emphasis on personalized and patient-centered medical practices. This shift recognizes patients as unique individuals rather than mere collections of symptoms. Within this framework, Digital Twin is emerging as a promising tool for creating dynamic, real-time virtual models of patient pathophysiology, allowing healthcare professionals to tailor interventions to each patient's unique needs. However, successfully adopting this technology in clinical settings requires addressing the complexities of implementation, from design to deployment. Many digital health solutions have shown tangible benefits, but the current trend often emphasizes process efficiency over fully leveraging the data semantics within modern systems. This focus can diminish the advantages of a person-centered approach, impacting care quality and patient satisfaction. Digital Twins bridge this gap by prioritizing data, which is essential for simulations and predictive analyses that anticipate a patient’s condition progression. This enables proactive and personalized interventions, enhancing patient compliance and significantly reducing diagnostic tests, referrals, and malpractice complaints. Furthermore, real-time digital replicas of individuals allow patients to actively participate in their care, transforming decision-making, especially in critical medical situations and chronic home care settings. Notably, research underscores the importance of semantic web technologies, such as knowledge graphs and ontologies, in structuring clinical data into machine-readable formats, forming the foundation of effective Digital Twin systems. While Digital Twin technology shows immense promise, barriers to adoption remain. From the legal frameworks surrounding medical device regulations and data privacy laws (e.g., GDPR) to stakeholder engagement and the need for explainable, trustworthy AI embedded within Digital Twins, a comprehensive approach is essential for successful implementation. Furthermore, attention to the interoperability of these technologies with legacy systems and the capacity to automate and personalize care for home-bound patients is crucial for fostering widespread adoption. This Research Topic seeks to explore innovative approaches that unlock the full potential of Digital Twins for accelerating the transition towards modern, person-centric healthcare. By assembling contributions from diverse interdisciplinary perspectives, we aim to harness the collective expertise to propel the evolution of healthcare delivery.This research topic welcomes all article types accepted by Frontiers in Digital Health. Suitable themes for manuscripts include:• Digital Twin engineering for personalized healthcare applications: Focuses on developing and applying Digital Twins tailored for individual patients, integrating real-time physiological, genetic, and environmental data to create accurate virtual models that simulate and predict health outcomes. • Trustworthy embedded AI: Emphasizes integrating transparent, reliable AI within Digital Twins to enhance their predictive and diagnostic capabilities while ensuring data security, algorithmic fairness, and compliance with regulatory standards. • Ontologies and Explainable AI in human-centric healthcare systems: Examines how ontologies and semantic web technologies contribute to the implementation of Digital Twins by enhancing the transparency and interpretability of AI models. This theme explores how structured ontologies can support the integration of Digital Twins into healthcare systems by providing a standardized framework for understanding clinical data, predictions, and decisions, thus improving the reliability and effectiveness of their applications in practice. • Leveraging data-driven technologies to optimize care for home-bound patients: Investigates the role of Digital Twins in conjunction with advanced data analytics, remote monitoring systems, and wearable devices to enhance the care of homebound patients. This theme focuses on how Digital Twin technology can reduce hospital readmissions and improve quality of life by integrating real-time data into actionable insights for home-based care. • Patient engagement through pervasive health informatics solutions: Focuses on integrating Digital Twins into user-friendly health informatics tools like mobile apps and patient portals to enhance patient engagement. This theme explores how Digital Twins can foster patient education, improve communication, and support treatment adherence by providing personalized and actionable health insights. • Case studies and real-world applications demonstrating the impact of person centric healthcare approaches exploiting data-driven technologies: Seeks examples of successful implementations of Digital Twins, AI, and digital health tools, highlighting outcomes, lessons learned, and best practices. • Enhancing Interoperability and Automating Healthcare Delivery through Data Standardization and EHR Implementation: Focuses on converting health data to standardized formats and implementing Electronic Health Records (EHRs) to replace clipboards, reduce provider burden, feed Digital Twins, and trigger autonomous processes, with an emphasis on improving interoperability across healthcare systems.